Federated learning (FL) has received considerable attention because it allows multiple devices to train models locally without revealing sensitive data. Well-trained local models are transmitted to a parameter server for further aggregation. The dependence on a trusted central server makes FL vulnerable to the single point of failure or attack. Blockchain is regarded as a state-of-the-art solution to decentralize the central server and provide attractive features simultaneously, such as immutability, traceability, and accountability. However, current popular blockchain systems cannot be combined with FL seamlessly. Since all local models should be collected before aggregation, the latency of FL is determined by the slowest device. The consensus process required by blockchain will increase the latency further, especially, when a large block is required for including the model. Moreover, forever-growing blockchain together with models will take up a lot of storage space, making it impractical to be deployed on lightweight devices. To address these problems, we propose a lightweight blockchain TORR for FL. A novel consensus protocol Proof of Reliability is designed to achieve fast consensus while mitigating the impact of stragglers. A storage protocol is designed based on erasure coding and periodic storage refreshing policy. With erasure coding, we take full advantage of the limited storage space of devices. With the periodic storage refreshing policy, we reduce the requirement for storage. Compared to the common blockchain-based FL system, TORR reduces the system latency, overall storage overhead, and peak storage overhead by up to 62%, 75.44%, and 51.77%, respectively.
Cross-silo federated learning (FL) is promising in facilitating data collaboration across various organizations, which greatly alleviates the information silo problem in industries and promotes the data intelligence of Internet of Things. With the advances of decentralized FL, the higher requirements of trust and privacy are put forward. Traditional FL heavily relies on a central coordinating server, which suffers from single points of failure and lacks trust in the correctness of aggregation results. What is more, the intrinsic privacy issues of FL have aroused public attention, such as gradient inversion attack in local gradients. However, the privacy of quantized gradients remains serious and lacks attention, especially the most extremely 1-bit quantization in sign-based FL. In this article, we demonstrate the potential privacy risk in sign-based FL by presenting a new gradient inversion attack, which successfully restores the original data from sign-based quantized gradients. And then we tackle the above two challenges via constructing a self-aggregation privacy-preserving FL atop blockchain, which takes advantage of a variant of ElGamal encryption to protect the privacy of local sign-based quantized gradients, and leverages the smart contract to achieve secure self-aggregation for participants without involving a centralized server. Moreover, we analyze that the proposed protocol achieves privacy and public verifiability. Finally, we evaluate the performance of the proposed protocol with a real deep learning model, and the results show that our protocol is resilient against gradient inversion attack in a decentralized environment without sacrificing learning accuracy.
Federated learning is an efficient technology that implements distributed model training among multiple data sources with local data, and can realize data privacy protection and data sharing computing .However, existing federated learning models may involve a large number of external attacks that can reconstruct the original training data using the acquired model, resulting in possible global model or user privacy data attacks. To address the above problem, we propose a new decentralized multiconsensus federated learning model by combining blockchain and interplanetary file system (IPFS), named as BIFLC. To be specific, we firstly design an on-chain consensus process based on a blockchain hybrid consensus mechanism by introducing a proof- of-work (PoW) and a proof-of-stake (PoS) mechanism, which can ensure the integrity of the on-chain consensus process and provide a chained data hash index for data. Furthermore, we introduce the interplanetary file system to reduce the cost of storing data on the chain and employ its distributed content delivery mechanism to save bandwidth. Extensive experiments demonstrate that our proposed scheme has higher accuracy and lower IPFS transmission time.
Cong T. Nguyen, Dinh Thai Hoang, Diep N. Nguyen, Yong Xiao · 6 authors
Due to its security, transparency, and flexibility in verifying virtual assets, blockchain has been identified as one of the key technologies for Metaverse. Unfortunately, blockchain-based Metaverse faces serious challenges such as massive resource demands, scalability, and security/privacy concerns. To address these issues, this paper proposes a novel sharding-based blockchain framework, namely MetaShard, for Metaverse applications. Particularly, we first develop an effective consensus mechanism, namely Proof-of-Engagement, that can incentivize MUs' data and computing resource contribution. Moreover, to improve the scalability of MetaShard, we propose an innovative sharding management scheme to maximize the network's throughput while protecting the shards from 51% attacks. Since the optimization problem is NP-complete, we develop a hybrid approach that decomposes the problem (using the binary search method) into sub-problems that can be solved effectively by the Lagrangian method. As a result, the proposed approach can obtain solutions in polynomial time, thereby enabling flexible shard reconfiguration and reducing the risk of corruption from the adversary. Extensive numerical experiments show that, compared to the state-of-the-art commercial solvers, our proposed approach can achieve up to 66.6% higher throughput in less than 1/30 running time. Moreover, the proposed approach can achieve global optimal solutions in most experiments.
Since the concern of privacy leakage extremely discourages user participation in sharing data, federated learning has gradually become a promising technique for both academia and industry for achieving collaborative learning without leaking information about the local data. Unfortunately, most federated learning solutions cannot efficiently verify the execution of each participant's local machine learning model and protect the privacy of user data, simultaneously. In this article, we first propose a Zero-Knowledge Proof-based Federated Learning (ZKP-FL) scheme on blockchain. It leverages zero-knowledge proof for both the computation of local data and the aggregation of local model parameters, aiming to verify the computation process without requiring the plaintext of the local data. We further propose a Practical ZKP-FL (PZKP-FL) scheme to support fraction and non-linear operations. Specifically, we explore a Fraction-Integer mapping function, and use Taylor expansion to efficiently handle non-linear operations while maintaining the accuracy of the federated learning model. We also analyze the security of PZKP-FL. Performance analysis demonstrates that the whole running time of the PZKP-FL scheme is approximately less than one minute in parallel execution.
We propose an application for near-term quantum devices: namely, generating cryptographically certified random bits, to use (for example) in proof-of-stake cryptocurrencies. Our protocol repurposes the existing "quantum supremacy" experiments, based on random circuit sampling, that Google and USTC have successfully carried out starting in 2019. We show that, whenever the outputs of these experiments pass the now-standard Linear Cross-Entropy Benchmark (LXEB), under plausible hardness assumptions they necessarily contain $Ω(n)$ min-entropy, where $n$ is the number of qubits. To achieve a net gain in randomness, we use a small random seed to produce pseudorandom challenge circuits. In response to the challenge circuits, the quantum computer generates output strings that, after verification, can then be fed into a randomness extractor to produce certified nearly-uniform bits -- thereby "bootstrapping" from pseudorandomness to genuine randomness. We prove our protocol sound in two senses: (i) under a hardness assumption called Long List Quantum Supremacy Verification, which we justify in the random oracle model, and (ii) unconditionally in the random oracle model against an eavesdropper who could share arbitrary entanglement with the device. (Note that our protocol's output is unpredictable even to a computationally unbounded adversary who can see the random oracle.) Currently, the central drawback of our protocol is the exponential cost of verification, which in practice will limit its implementation to at most $n\sim 60$ qubits, a regime where attacks are expensive but not impossible. Modulo that drawback, our protocol appears to be the only practical application of quantum computing that both requires a QC and is physically realizable today.
Federated learning (FL) has been widely used in both academia and industry all around the world. FL has advantages from the perspective of data security, data diversity, real-time continual learning, hardware efficiency, etc. However, it brings new privacy challenges, such as membership inference attacks and data poisoning attacks, when parts of participants are not assumed to be fully honest. Moreover, selfish participants can obtain others’ collaborative data but do not contribute their real local data or even provide fake data. This violates the fairness of FL schemes. Therefore, advanced privacy and fairness techniques have been integrated into FL schemes including blockchain, differential privacy, zero-knowledge proof, etc. However, most of the existing works still have room to enhance the practicality due to our exploration. In this paper, we propose a Blockchain-based Pseudorandom Number Generation (BPNG) protocol based on Verifiable Random Functions (VRFs) to guarantee the fairness for FL schemes. Next, we further propose a Gradient Random Noise Addition (GRNA) protocol based on differential privacy and zero-knowledge proofs to protect data privacy for FL schemes. Finally, we implement both two protocols on Hyperledger Fabric and analyze their performance. Simulation experiments show that the average time that proof generation takes is 18.993 s and the average time of on-chain verification is 2.27 s under our experimental environment settings, which means the scheme is practical in reality.
Federated learning (FL) paradigms aim to amalgamate diverse data properties stored locally at each user, while preserving data privacy through sharing users’ learning experiences and iteratively aggregating their local learning models into a global one. However, the majority of FL architectures with centralized cloud do not guarantee the trust in sharing users’ models, and hence, open the door for slowing and/or contaminating the global learning experience. In this paper, we propose a decentralized Blockchain (BC)-based framework and define a comprehensive protocol for exchanging local models, in order to guarantee users’ mutual trust while sharing their local learning experiences. We then propose a technique to optimize the global learning experience using Reinforcement Learning (RL), namely RL-FL-BC, to tackle the trade-off between information age of the learning parameters, data skewness (i.e., non-iid), and BC transaction cost (i.e., Ether price). We implement the proposed framework in a realistic containerized environment to facilitate the comparative study of the RL-FL-BC technique with baselines techniques. Our results show the efficacy of the BC-based protocol to facilitate the exchange of both the models’ and the optimization parameters to guarantee users’ mutual trust, while improving global learning performance compared to baselines techniques.
The broad usage of images in real-time applications demands a cloud infrastructure due to its advantages. Many use cases are built where the image data is shared, sharing becomes the core function, and the medical domain takes its broad advantage. The cloud is a centralized infrastructure for its all-operation usages; it depends mainly on the trusted third party to handle security concerns. Therefore, the privacy preservation of the image data or any data becomes an issue of concern. The distrusted system advantages are achieved using blockchain technology for image data security and privacy concerns. The traditional approaches of the security and privacy models raise many apprehensions as these are designed on the centralized systems of the data sharing mechanisms. It is also observed that large data files are not wisely handled, which demands building a framework model that takes image data and any other data of any size to ensure a dependable optimal security system. This paper presents a framework model to achieve optimal time complexity for securing the privacy aspects of the image data or any other data that uses space optimal file system using distributed security mechanism for both the storage and sharing of the data. The proposed framework model for optimal time complexity and security uses a duplication algorithm using stakeholder agreement to ensure efficient access control to the resources using the cryptographic approach to the Ethereum ecosystem. The performance metric used in the model evaluation includes the degree of availability and efficiency. On benchmarks, it performs well compared to the traditional cloud-built distributed systems. The quantified outcome of the proposed scheme exhibits a 42.5% of reduction in time for data repositioning, a 41.1% of reduction in time for data retrieval, a 34.8% of reduction in operational cost, a 73.9% of reduction in delay, and a 61% faster algorithm execution time in contrast to conventional blockchain method.
Blockchains with smart contracts are distributed ledger systems that achieve block-state consistency among distributed nodes by only allowing deterministic operations of smart contracts. However, the power of smart contracts is enabled by interacting with stochastic off-chain data, which in turn opens the possibility to undermine the block-state consistency. To address this issue, an oracle smart contract is used to provide a single consistent source of external data; but, simultaneously, this introduces a single point of failure, which is called the oracle problem. To address the oracle problem, we propose an adaptive conformal consensus (ACon$^2$) algorithm that derives a consensus set of data from multiple oracle contracts via the recent advance in online uncertainty quantification learning. Interesting, the consensus set provides a desired correctness guarantee under distribution shift and Byzantine adversaries. We demonstrate the efficacy of the proposed algorithm on two price datasets and an Ethereum case study. In particular, the Solidity implementation of the proposed algorithm shows the potential practicality of the proposed algorithm, implying that online machine learning algorithms are applicable to address security issues in blockchains.
Subhash Shankar Pandey, Tadasha Dash, Prasanta K. Panigrahi, Ahmed Farouk
Blockchain is a decentralized system that allows transaction transmission and storage according to the roles of the Consensus algorithm and Smart contracts. Non-fungible tokens (NFTs) consolidate the best characteristics of blockchain technology to deliver unique and bona fide tokens, each with distinctive attributes with non-fungible resources. Unfortunately, current classical NFTs are suffering from high costs regarding the consumed power of mining and lack of security. Therefore, this paper presents a new protocol for preparing quantum non-fungible tokens where a quantum state representing NFT is mounted on a blockchain instead of physically giving it to the owner. The proposed scheme is simulated and analyzed against various attacks and proves its ability to secure against them. Furthermore, the presented protocol provides reliable and cheaper NFTs than the classical one.
Recently, blockchain-based federated learning (BFL) has attracted intensive research attention due to that the training process is auditable and the architecture is serverless avoiding the single point failure of the parameter server in vanilla federated learning (VFL). Nevertheless, BFL tremendously escalates the communication traffic volume because all local model updates (i.e., changes of model parameters) obtained by BFL clients will be transmitted to all miners for verification and to all clients for aggregation. In contrast, the parameter server and clients in VFL only retain aggregated model updates. Consequently, the huge communication traffic in BFL will inevitably impair the training efficiency and hinder the deployment of BFL in reality. To improve the practicality of BFL, we are among the first to propose a fast blockchain-based communication-efficient federated learning framework by compressing communications in BFL, called BCFL. Meanwhile, we derive the convergence rate of BCFL with non-convex loss. To maximize the final model accuracy, we further formulate the problem to minimize the training loss of the convergence rate subject to a limited training time with respect to the compression rate and the block generation rate, which is a bi-convex optimization problem and can be efficiently solved. To the end, to demonstrate the efficiency of BCFL, we carry out extensive experiments with standard CIFAR-10 and FEMNIST datasets. Our experimental results not only verify the correctness of our analysis, but also manifest that BCFL can remarkably reduce the communication traffic by 95-98% or shorten the training time by 90-95% compared with BFL.
The fast proliferation of digital twin (DT) establishes a direct connection between the physical entity and its deployed digital representation. As markets shift toward mass customization and new service delivery models, the digital representation has become more adaptive and agile by forming digital twin networks (DTNs). The DTN institutes a real-time single source of truth everywhere. However, there are several issues preventing DTNs from further application, including centralized processing, data falsification, privacy leakage, lack of incentive mechanism, and so on. To make DTN better meet the ever changing demands, we propose a novel block-chain-enabled adaptive asynchronous federated learning (FedTwin) paradigm for privacy-preserving and decentralized DTNs. We design Proof-of-Federalism (PoF), which is a tailor-made consensus algorithm for autonomous DTNs. In each DT's local training phase, generative adversarial network enhanced differential privacy is used to protect the privacy of local model parameters, while a modified Isolation Forest is deployed to filter out the falsified DTs. In the global aggregation phase, an improved Markov decision process is leveraged to select optimal DTs to achieve adaptive asynchronous aggregation while providing a rollback mechanism to redact the falsified global models. With this article, we aim to provide insights to forthcoming researchers and readers in this under-explored domain.
Muhammad Firdaus, Harashta Tatimma Larasati, Kyung-Hyune Rhee
Federated learning (FL) has considerably emerged as a promising solution to enhance user privacy and data security by enabling collaboratively multi-party model learning without exchanging confidential data. Nevertheless, most existing FL approaches still rely on a central server to obtain a global model by collecting all uploaded models from participants, which may lead to several threats from malicious participants and even expose participant privacy. Therefore, to tackle these problems, we proposed a secure FL framework by empowering blockchain to replace the centralized aggregator sever and utilize Differential Privacy (DP) to address various attacks, e.g., membership inference attacks, during the collaborative FL model training process. The proposed framework has been implemented through two scenarios, i.e., blockchain-based FL to form a decentralized system and DP-based FL to construct the randomized privacy protection using the IBM DP Library.
Truc Nguyen, Phuc Thai, Tre’ R. Jeter, Thang N. Dinh · 5 authors
Despite the great potential of Federated Learning (FL) in large-scale distributed learning, the current system is still subject to several privacy issues due to the fact that local models trained by clients are exposed to the central server. Consequently, secure aggregation protocols for FL have been developed to conceal the local models from the server. However, we show that, by manipulating the client selection process, the server can circumvent the secure aggregation to learn the local models of a victim client, indicating that secure aggregation alone is inadequate for privacy protection. To tackle this issue, we leverage blockchain technology to propose a verifiable client selection protocol. Owing to the immutability and transparency of blockchain, our proposed protocol enforces a random selection of clients, making the server unable to control the selection process at its discretion. We present security proofs showing that our protocol is secure against this attack. Additionally, we conduct several experiments on an Ethereum-like blockchain to demonstrate the feasibility and practicality of our solution.
Τα τελευταία χρόνια έχει γίνει αντιληπτό ότι ο αποδοτικότερος τρόπος να εκπαιδευτεί κάποιο σύνθετο μοντέλο τεχνητής νοημοσύνης είναι η αξιοποίηση εξαιρετικά μεγάλων όγκων δεδομένων και μεγάλης υπολογιστικής ισχύος. Το γεγονός αυτό δίνει ένα ανυπέρβλητο προβάδισμα στις λίγες εταιρίες που κατέχουν αυτά τα στοιχεία, με αποτέλεσμα αυτές να τείνουν να κυριαρχήσουν στις εξελίξεις. Συνεπώς θα ήταν επιθυμητό να δημιουργήσουμε πρότυπα τα οποία: 1) Εγκαταλείπουν τη λογική των μεγάλων ιδιωτικών υπολογιστικών κέντρων. 2) Βασίζονται, όχι απλώς σε αποκεντρωμένο, αλλά σε πλήρως κατανεμημένο μοντέλο, υπό την έννοια ότι λειτουργούν δίχως την ανάγκη ενός κεντρικού συντονιστή. Ακολουθώντας αυτή την οδό μπορούν να δημιουργηθούν αρχιτεκτονικές οι οποίες: 1) Αξιοποιώντας πολυάριθμους κόμβους προσεγγίζουν (η ακόμα και ξεπερνούν) την υπολογιστική ισχύ ενός μεγάλου data center. 2) Λόγω ισοτιμίας των συμμετεχόντων δεν έχουν "single point of failure", το οποίο πρακτικά σημαίνει ότι η λειτουργία τους συνεχίζεται απρόσκοπτα ακόμα και μετά την αποσύνδεση ενός μεγάλου ποσοστού των κόμβων τους. 3) Είναι "εκδημοκρατισμένες", δηλαδή όλοι οι συμμετέχοντες έχουν ίση πρόσβαση στα παραγόμενα αποτελέσματα. Οι μέχρι τώρα υλοποιήσεις των αλγορίθμων ανάπτυξης νευρωνικών δικτύων επικεντρώνονται πρωτίστως στον τομέα της ταχύτητας εκπαίδευσης του μοντέλου. Παρόλο που έχουν αναπτυχθεί εκδοχές για κατανεμημένη επεξεργασία, αυτές έχουν ανάγκη από έναν κεντρικό κόμβο-διαχειριστή ο οποίος θα αναλαμβάνει τον συντονισμό των υπολοίπων. Δεν έχει όμως γίνει επαρκής μελέτη για μία υλοποίηση που δεν απαιτεί έναν τέτοιο διακομιστή. Η πρότασή μας αξιοποιεί νέας τεχνολογίας ομότιμες τοπολογίες που έχουν ως θεμέλιο λίθο την κρυπτογραφική επαλήθευση (Distributed Ledger Technology - DLT), με στόχο τη "διάχυση" του διαχειριστικού ρόλου σε ολόκληρο το δίκτυο. Κατά την εκπόνηση της έρευνας δοκιμάστηκαν διαφορετικές διαμορφώσεις της κατανεμημένης λειτουργίας και αποτιμήθηκαν οι επιπτώσεις που είχε αυτή η παραμετροποίηση, τόσο στην ταχύτητα εκπαίδευσης όσο και στην ποιότητα του παραγόμενου μοντέλου. Το αποτέλεσμα είναι ένα καινοτόμο οικοσύστημα νεοφυών τεχνολογιών, για εκδημοκρατισμένη εκπαίδευση βαθέων νευρωνικών δικτύων.
Federated learning is known to be vulnerable to both security and privacy issues. Existing research has focused either on preventing poisoning attacks from users or on concealing the local model updates from the server, but not both. However, integrating these two lines of research remains a crucial challenge since they often conflict with one another with respect to the threat model. In this work, we develop a principle framework that offers both privacy guarantees for users and detection against poisoning attacks from them. With a new threat model that includes both an honest-but-curious server and malicious users, we first propose a secure aggregation protocol using homomorphic encryption for the server to combine local model updates in a private manner. Then, a zero-knowledge proof protocol is leveraged to shift the task of detecting attacks in the local models from the server to the users. The key observation here is that the server no longer needs access to the local models for attack detection. Therefore, our framework enables the central server to identify poisoned model updates without violating the privacy guarantees of secure aggregation.
Sikha Pentyala, Davis Railsback, Ricardo Maia, Rafael Dowsley · 7 authors
In the classical setting of differential privacy, a privacy-preserving query is performed on a private database, after which the query result is released to the analyst; a differentially private query ensures that the presence of a single database entry is protected from the analyst’s view. In this work, we contribute the first definitional framework for differential privacy in the trusted curator setting (Fig. 1); clients submit private inputs to the trusted curator, which then computes individual outputs privately returned to each client. The adversary is more powerful than the standard setting; it can corrupt up to n-1 clients and subsequently decide inputs and learn outputs of corrupted parties. In this setting, the adversary also obtains leakage from the honest output that is correlated with a corrupted output. Standard differentially private mechanisms protect client inputs but do not mitigate output correlation leaking arbitrary client information, which can forfeit client privacy completely. We initiate the investigation of a novel notion of correlated-output differential privacy to bound the leakage from output correlation in the trusted curator setting. We define the satisfaction of both standard and correlated-output differential privacy as round differential privacy and highlight the relevance of this novel privacy notion to all application domains in the trusted curator model. \nWe explore round differential privacy in traditional "dark pool" market venues, which promise privacy-preserving trade execution to mitigate front-running; privately submitted trade orders and trade execution are kept private by the trusted venue operator. We observe that dark pools satisfy neither classic nor correlated-output differential privacy; in markets with low trade activity, the adversary may trivially observe recurring, honest trading patterns, and anticipate and front-run future trades. In response, we present the first round differentially private market mechanisms that formally mitigate information leakage from all trading activity of a user. This is achieved with fuzzy order matching, inspired by the standard randomized response mechanism; however, this also introduces a liquidity mismatch as buy and sell orders are not guaranteed to execute pairwise, thereby weakening output correlation; this mismatch is compensated for by a round differentially private liquidity provider mechanism, which freezes a noisy amount of assets from the liquidity provider for the duration of a privacy epoch, but leaves trader balances unaffected. We propose oblivious algorithms for realizing our proposed market mechanisms with secure multi-party computation (MPC) and implement these in the Scale-Mamba Framework using Shamir Secret Sharing based MPC. We demonstrate practical, round differentially private trading with comparable throughput as prior work implementing (traditional) dark pool algorithms in MPC; our experiments demonstrate practicality for both traditional finance and decentralized finance settings.
Jiasi Weng, Jian Weng, Gui Hong Tang, Anjia Yang · 6 authors
We propose a new approach for privacy-preserving and verifiable convolutional neural network (CNN) testing in a distrustful multi-stakeholder environment. The approach is aimed to enable that a CNN modeldeveloperconvinces auserof the truthful CNN performance over non-public data frommultiple testers, while respecting model and data privacy. To balance the security and efficiency issues, we appropriately integrate three tools with the CNN testing, including collaborative inference, homomorphic encryption (HE) and zero-knowledge succinct non-interactive argument of knowledge (zk-SNARK). We start with strategically partitioning a CNN model into a private part kept locally by the model developer, and a public part outsourced to an outside server. Then, the private part runs over the HE-protected test data sent by a tester, and transmits its outputs to the public part for accomplishing subsequent computations of the CNN testing. Second, the correctness of the above CNN testing is enforced by generating zk-SNARK based proofs, with an emphasis on optimizing proving overhead for two-dimensional (2-D) convolution operations, since the operations dominate the performance bottleneck during generating proofs. We specifically present a new quadratic matrix program (QMP)-based arithmetic circuit witha single multiplication gatefor expressing 2-D convolution operations between multiple filters and inputs in a batch manner. Third, we aggregate multiple proofs with respect to a same CNN model but different testers’ test data (i.e., different statements) into one proof, and ensure that the validity of the aggregated proof implies the validity of the original multiple proofs. Lastly, our experimental results demonstrate that our QMP-based zk-SNARK performs nearly 13.9× faster than the existing quadratic arithmetic program (QAP)-based zk-SNARK in proving time, and 17.6× faster in Setup time, for high-dimension matrix multiplication. Besides, the limitation on handling a bounded number of multiplications of QAP-based zk-SNARK is relieved.
Federated learning enables clients to train a machine learning model jointly without sharing their local data. However, due to the centrality of federated learning framework and the untrustworthiness of clients, traditional federated learning solutions are vulnerable to poisoning attacks from malicious clients and servers. In this paper, we aim to mitigate the impact of the central server and malicious clients by designing a Privacy-preserving Byzantine-robust Federated Learning (PBFL) scheme based on blockchain. Specifically, we use cosine similarity to judge the malicious gradients uploaded by malicious clients. Then, we adopt fully homomorphic encryption to provide secure aggregation. Finally, we use blockchain system to facilitate transparent processes and implementation of regulations. Our formal analysis proves that our scheme achieves convergence and provides privacy protection. Our extensive experiments on different datasets demonstrate that our scheme is robust and efficient. Even if the root dataset is small, our scheme can achieve the same efficiency as FedSGD.
The rapid expansion of data worldwide invites the need for more distributed solutions in order to apply machine learning on a much wider scale. The resultant distributed learning systems can have various degrees of centralization. In this work, we demonstrate our solution FLoBC for building a generic decentralized federated learning system using the blockchain technology, accommodating any machine learning model that is compatible with gradient descent optimization. We present our system design comprising the two decentralized actors: trainer and validator, alongside our methodology for ensuring reliable and efficient operation of said system. Finally, we utilize FLoBC as an experimental sandbox to compare and contrast the effects of trainer-to-validator ratio, reward-penalty policy, and model synchronization schemes on the overall system performance, ultimately showing by example that a decentralized federated learning system is indeed a feasible alternative to more centralized architectures.