Incentives that compensate for the involved costs in the decentralized training of a Federated Learning (FL) model act as a key stimulus for clients' long-term participation. However, it is challenging to convince clients for quality participation in FL due to the absence of: (i) full information on the client's data quality and properties; (ii) the value of client's data contributions; and (iii) the trusted mechanism for monetary incentive offers. This often leads to poor efficiency in training and communication. While several works focus on strategic incentive designs and client selection to overcome this problem, there is a major knowledge gap in terms of an overall design tailored to the foreseen digital economy, including Web 3.0, while simultaneously meeting the learning objectives. To address this gap, we propose a contribution-based tokenized incentive scheme, namely \texttt{FedToken}, backed by blockchain technology that ensures fair allocation of tokens amongst the clients that corresponds to the valuation of their data during model training. Leveraging the engineered Shapley-based scheme, we first approximate the contribution of local models during model aggregation, then strategically schedule clients lowering the communication rounds for convergence and anchor ways to allocate \emph{affordable} tokens under a constrained monetary budget. Extensive simulations demonstrate the efficacy of our proposed method.
In peer-to-peer (P2P) energy trading, a secured infrastructure is required to manage trade and record monetary transactions. A central server/authority can be used for this. But there is a risk of central authority influencing the energy price. So blockchain technology is being preferred as a secured infrastructure in P2P trading. Blockchain provides a distributed repository along with smart contracts for trade management. This reduces the influence of central authority in trading. However, these blockchain-based systems still rely on a central authority to pair/match sellers with consumers for trading energy. The central authority can interfere with the matching process to profit a selected set of users. Further, a centralized authority also charges for its services, thereby increasing the cost of energy. We propose two distributed mechanisms to match sellers with consumers. The first mechanism doesn't allow for price negotiations between sellers and consumers, whereas the second does. We also calculate the time complexity and the stability of the matching process for both mechanisms. Using simulation, we compare the influence of centralized control and energy prices between the proposed and the existing mechanisms. The overall work strives to promote the free market and reduce energy prices.
Proof-of-Work (PoW) is a popular blockchain consensus algorithm that is used in cryptocurrencies like Bitcoin in which hashing operations are repeated until the resulting hash has certain properties. This approach uses lots of computational power and energy for the sole purpose of securing the blockchain. In order to not waste energy on hashing operations that do not have any other purpose than enabling consensus between nodes and therefore securing the blockchain, Proof-of-Useful-Work (PoUW) is an alternative approach which aims to replace excessive usage of hash functions with tasks that bring additional real-world benefit, e.g. supporting scientific experiments that rely on computationally heavy simulations. This publication consists of two parts: In the first part, important properties of conventional hash-based PoW are described. In the second part, theoretical PoUW concepts such as Coinami, CoinAI and the first successful PoUW cryptocurrency Primecoin are analyzed with respects to how PoW properties can be retained while doing useful work.
Maximal (also miner) extractable value, or MEV, usually refers to the value that privileged players can extract by strategically ordering, censoring, and placing transactions in a blockchain. Each blockchain network, which we refer to as a domain, has its own consensus, ordering, and block-creation mechanisms, which gives rise to different optimal strategies to extract MEV. The strategic behaviour of rational players, known as searchers, lead to MEV games that have different impacts and externalities in each domain. Several ordering mechanisms, which determine the inclusion and position of transactions in a block, have been considered to construct alternative games to organise MEV extraction, and minimize negative externalities; examples include sealed bid auctions, first input first output, and private priority gas auctions. However, to date, no sufficiently formal and abstract definition of MEV games have been made. In this paper, we take a step toward the formalization of MEV games and compare different ordering mechanisms and their externalities. In particular, we attempt to formalize games that arise from common knowledge MEV opportunities, such as arbitrage and sandwich attacks. In defining these games, we utilise a theoretical framework that provides groundwork for several important roles and concepts, such as the searcher, sequencer, domain, and bundle. We also introduce the price of MEV as the price of anarchy of MEV games, a measure that provides formal comparison between different ordering mechanisms.
Yuntao Wang, Haixia Peng, Zhou Su, Tom H. Luan · 6 authors
Proof of work (PoW), as the representative consensus protocol for blockchain, consumes enormous amounts of computation and energy to determine bookkeeping rights among miners but does not achieve any practical purposes. To address the drawback of PoW, we propose a novel energy-recycling consensus mechanism named platform-free proof of federated learning (PF-PoFL), which leverages the computing power originally wasted in solving hard but meaningless PoW puzzles to conduct practical federated learning (FL) tasks. Nevertheless, potential security threats and efficiency concerns may occur due to the untrusted environment and miners’ self-interested features. In this paper, by devising a novel block structure, new transaction types, and credit-based incentives, PF-PoFL allows efficient artificial intelligence (AI) task outsourcing, federated mining, model evaluation, and reward distribution in a fully decentralized manner, while resisting spoofing and Sybil attacks. Besides, PF-PoFL equips with a user-level differential privacy mechanism for miners to prevent implicit privacy leakage in training FL models. Furthermore, by considering dynamic miner characteristics (e.g., training samples, non-IID degree, and network delay) under diverse FL tasks, a federation formation game-based mechanism is presented to distributively form the optimized disjoint miner partition structure with Nash-stable convergence. Extensive simulations validate the efficiency and effectiveness of PF-PoFL.
The dominance of a few big companies in the storage market arising various concerns including single point of failure, privacy violation, and oligopoly. To eliminate the dependency on such a centralized storage architecture, several Decentralized Storage Network (DSN) schemes such as Filecoin, Sia, and Storj have been introduced. DSNs leverage blockchain technology to create a storage platform such that the micro storage providers can also participate in the storage market. To verify the accurate data storage by the storage providers during a storage contract, DSNs apply a Proof of Storage (PoS) scheme to continuously inspect the storage service. However, continuous verification of the storage provider imposes an extra cost to the network and therefore end-users. Moreover, DSN's PoS verification is vulnerable to a service denying attack in which the storage provider submits valid PoS to the network while denying the service to the client. Considering the benefits and existing challenges of DSNs, this paper introduces a novel incentive-compatible DSN scheme. In this scheme, the PoS is conducted only if the client submits a challenge request. We model the storage service as a non-cooperative repeated dynamic game and set the players' payoffs such that the storage provider's dominant strategy is to honestly follow the storage contract. Our proposed mechanism leverages the smart-contract and oracle network to govern the storage agreement between the client and storage provider efficiently. Furthermore, our scheme is independent of a specific blockchain platform but can be plugged into any blockchain platform with smart-contract execution capability. As a proof of concept, we have implemented our scheme using solidity language and chainlink oracle network. The performance analysis demonstrates the applicability of our scheme. The outcome of this paper is a new incentive-compatible mechanism designed carefully for the blockchain-based DSN. The proposed mechanism utilizes different tools including game-theory, smart-contract, oracle network, and Merkle tree to improve the security and performance of storage verification in DSN.
We study the competition between blockchains in a \emph{multi-chain} environment, where a dominant EVM-compatible blockchain (e.g., Ethereum) co-exists with an alternative EVM-compatible (e.g., Avalanche) and an EVM-incompatible (e.g., Algorand) blockchain. While EVM compatibility allows existing Ethereum users and developers to migrate more easily over to the alternative layer-1, EVM incompatibility might allow the firms to build more loyal and ``sticky'' user base, and in turn a more robust ecosystem. As such, the choice to be EVM-compatible is not merely a technological decision, but also an important strategic decision. In this paper, we develop a game theoretic model to study this competitive dynamic, and find that at equilibrium, new entrants/developers tend to adopt the dominant blockchain. To avoid adoption failure, the alternative blockchains have to either (1) directly subsidize the new entrant firms or (2) offer better features, which in practice can take form in lower transaction costs, faster finality, or larger network effects. We find that it is easier for EVM-compatible blockchains to attract users through direct subsidy, while it is more efficient for EVM-incompatible blockchains to attract users through offering better features/products.
Theo Diamandis, Alex Evans, Tarun Chitra, Guillermo Angeris
Public blockchains implement a fee mechanism to allocate scarce computational resources across competing transactions. Most existing fee market designs utilize a joint, fungible unit of account (e.g., gas in Ethereum) to price otherwise non-fungible resources such as bandwidth, computation, and storage, by hardcoding their relative prices. Fixing the relative price of each resource in this way inhibits granular price discovery, limiting scalability and opening up the possibility of denial-of-service attacks. As a result, many prominent networks such as Ethereum and Solana have proposed multi-dimensional fee markets. In this paper, we provide a principled way to design fee markets that efficiently price multiple non-fungible resources. Starting from a loss function specified by the network designer, we show how to compute dynamic prices that align the network's incentives (to minimize the loss) with those of the users and miners (to maximize their welfare), even as demand for these resources changes. Our pricing mechanism follows from a natural decomposition of the network designer's problem into two parts that are related to each other via the resource prices. These results can be used to efficiently set fees in order to improve network performance.
Decentralized computation outsourcing should allow anyone to access the large amounts of computational power that exists in the Internet of Things. Unfortunately, when trusted third parties are removed to achieve this decentralization, ensuring an outsourced computation is performed correctly remains a significant challenge. In this paper, we provide a solution to this problem. We outline Marvel DC, a fully decentralized blockchain-based distributed-computing protocol which formally guarantees that computers are strictly incentivized to correctly perform requested computations. Furthermore, Marvel DC utilizes a reputation management protocol to ensure that, for any minority of computers not performing calculations correctly, these computers are identified and selected for computations with diminishing probability. We then outline Privacy Marvel DC, a privacy-enhanced version of Marvel DC which decouples results from the computers which computed them, making the protocol suitable for computations such as Federated Learning, where results can reveal sensitive information about that computer that computed them. We provide an implementation of Marvel DC and analyses of both protocols, demonstrating that they are not only the first protocols to provide the aforementioned formal guarantees, but are also practical, competitive with prior attempts in the field, and ready to deploy.
Maximal Extractable Value (MEV) refers to excess value captured by miners (or validators) from users in a cryptocurrency network. This excess value often comes from reordering users' transactions to maximize fees or from inserting new transactions that front-run users' transactions. One of the most common types of MEV involves a `sandwich attack' against a user trading on a constant function market maker (CFMM), which is a popular class of automated market maker. We analyze game theoretic properties of MEV in CFMMs that we call \textit{routing} and \textit{reordering} MEV. In the case of routing, we present examples where the existence of MEV both degrades and, counterintuitively, \emph{improves} the quality of routing. We construct an analogue of the price of anarchy for this setting and demonstrate that if the impact of a sandwich attack is localized in a suitable sense, then the price of anarchy is constant. In the case of reordering, we show conditions when the maximum price impact caused by the reordering of sandwich attacks in a sequence of trades, relative to the average price, impact is $O(\log n)$ in the number of user trades. Combined, our results suggest methods that both MEV searchers and CFMM designers can utilize for estimating costs and profits of MEV.
Due to its minimal energy requirement the PoS consensus protocol has become an attractive alternative to PoW in modern cryptocurrencies. In this protocol the chance of being selected as a block proposer in each round is proportional to the current stake of any node. Thus, nodes with higher stakes will achieve more block rewards, resulting in the so-called rich-getting-richer problem. In this paper, we introduce a new block reward mechanism called the FRD (Fair Reward Distribution) mechanism, in which for each block produced, in addition to a major reward given to the block proposer, a small reward is given to all other nodes. We prove that this reward mechanism makes the PoS protocol fairer in terms of concentration of wealth by developing on the Bagchi-Pal urn model.
Kota Chin, Keita Emura, Kazumasa Omote, Shingo Sato
In an open-bid auction, a bidder can know the budgets of other bidders. Thus, a sealed-bid auction that hides bidding prices is desirable. However, in previous sealed-bid auction protocols, it has been difficult to provide a ``fund binding'' property, which would guarantee that a bidder has funds more than or equal to the bidding price and that the funds are forcibly withdrawn when the bidder wins. Thus, such protocols are vulnerable to false bidding. As a solution, many protocols employ a simple deposit method in which each bidder sends a deposit to a smart contract, which is greater than or equal to the bidding price, before the bidding phase. However, this deposit reveals the maximum bidding price, and it is preferable to hide this information. In this paper, we propose a sealed-bid auction protocol that provides a fund binding property. Our protocol not only hides the bidding price and a maximum bidding price, but also provides fund binding, simultaneously. For hiding the maximum bidding price, we pay attention to the fact that usual Ethereum transactions and transactions for sending funds to a one-time address have the same transaction structure, and it seems that they are indistinguishable. We discuss how much bidding transactions are hidden. We also employ DECO (Zhang et al,. CCS 2020) that proves the validity of the data to a verifier in which the data are taken from a source without showing the data itself. Finally, we give our implementation which shows transaction fees required and compare it to a sealed-bid auction protocol employing the simple deposit method.
While vaccinations continue to be rolled out to curb the ongoing COVID-19 pandemic, their verification is becoming a requirement for the re-incorporation of individuals into many social activities or travel. Blockchain technology has been widely proposed to manage vaccination records and their verification in many politically-bound regions. However, the high contagiousness of COVID-19 calls for a global vaccination campaign. Therefore, a blockchain for vaccination management must scale up to support such a campaign and be adaptable to the requirements of different countries. While there have been many proposals of blockchain frameworks that balance the access and immutability of vaccination records, their scalability, a critical feature, has not yet been addressed. In this paper, we propose a scalable and cooperative Global Immunization Information Blockchain-based System (GEOS) that leverages the global interoperability of immunization information systems. We model GEOS and describe its requirements, features, and operation. We analyze the communications and the delays incurred by the national and international consensus processes and blockchain interoperability in GEOS. Such communications are pivotal in enabling global-scale interoperability and access to electronic vaccination records for verification. We show that GEOS ably keeps up with the global vaccination rates of COVID-19 as an example of its scalability.
Directed Acyclic Graph (DAG) is a popular approach to achieve scalability of blockchain networks. Due to its high efficiency in data communication and great scalability, DAG has been widely adopted in many applications such as Internet of Things (IoT) and Decentralized Finance (DeFi). DAG-based blockchain, nevertheless, faces the key challenge of transaction inclusion collision due to the high concurrency and the network delay. Particularly, the transaction inclusion collision in DAG-based blockchain leads to the revenue and throughput dilemmas, which would greatly degrade the system performance. In this paper, we propose "TIPS", the Transaction Inclusion Protocol with Signaling, which broadcasts a signal indicating the transactions in the block. We show that with the prompt broadcast of a signal, TIPS substantially reduces the transaction collision and thus resolves these dilemmas. Moreover, we show that TIPS can defend against both the denial-of-service and the delay-of-service attacks. We also conduct intensive experiments to demonstrate the superior performance of the proposed protocol.
Reducing energy consumption is crucial not only to reduce OPEX but also to reduce the human debt to our planet. Over the past few years, most service providers (SPs) have actively tackled this issue, particularly targeting periods of low activity. Indeed, having fewer customers during these periods allows SPs to downsize or shut down part of their infrastructure. But this is not always optimal. Despite multiple energy-efficient optimizations, a mobile national operator (MNO) still need to maintain significant radio access network (RAN) infrastructure active at night. Could MNOs do better by cooperating with each other in such a way that an MNO can redirect its subscribers to a partner MNO, thus allowing its entire infrastructure to be temporarily deactivated while switching roles with the partner during a subsequent drop in activity period? To answer this question, we investigated a novel collaborative framework based on multi-agent reinforcement learning (MARL) allowing for negotiations between SPs as well as trustful reports from a distributed ledger technology (DLT) to evaluate the amount of energy saved. We leveraged it to experiment three different sets of rules (free, recommended, or imposed) regulating the negotiation between multiple SPs (3, 4, 8, or 10). Based on the observation of four cooperation metrics (efficiency, safety, incentive-compatibility, and fairness), the simulations showed that the imposed set of rules proved to be the best mode.
Jiawen Kang, Dongdong Ye, Jiangtian Nie, Jiang Xiao · 9 authors
The emerging industrial metaverses realize the map-ping and expanding operations of physical industry into virtual space for significantly upgrading intelligent manufacturing. The industrial metaverses obtain data from various production and operation lines by Industrial Internet of Things (IIoT), and thus conduct effective data analysis and decision-making, thereby en-hancing the production efficiency of the physical space, reducing operating costs, and maximizing commercial value. However, there still exist bottlenecks when integrating metaverses into IIoT, such as the privacy leakage of sensitive data with commercial secrets, IIoT sensing data freshness, and incentives for sharing these data. In this paper, we design a user-defined privacy-preserving framework with decentralized federated learning for the industrial metaverses. To further improve privacy protection of industrial metaverse, a cross-chain empowered federated learning framework is further utilized to perform decentralized, secure, and privacy-preserving data training on both physical and virtual spaces through a hierarchical blockchain architecture with a main chain and multiple subchains. Moreover, we introduce the age of information as the data freshness metric and thus design an age-based contract model to motivate data sensing among IIoT nodes. Numerical results indicate the efficiency of the proposed framework and incentive mechanism in the industrial metaverses.
We are often faced with the non-trivial task of designing incentive mechanisms in the era of Web3. As history has shown, many Web3 services failed mostly due to the lack of a rigorous incentive mechanism design based on token economics. However, traditional mechanism design, where there is an assumption that the users of services strategically make decisions so that their expected profits are maximized, often does not capture their real behavior well as it ignores humans' psychological bias in making decisions under uncertainty. In this paper, we propose an incentive mechanism design for crypto-enabled services using behavioral economics. Specifically, we take an example of a crypto lottery game in this work and incorporate a seminal work of cumulative prospect theory into its lottery game mechanism (or rule) design. We designed four mechanisms and compared them in terms of utility, a metric of how appealing a mechanism is to participants, and a game operator's expected profit. Our approach is generic and will be applicable to a wide range of crypto-based services where a decision has to be made under uncertainty.
Alyssa Blackburn, Christoph Huber, Yossi Eliaz, Muhammad Shahzads Shamim · 9 authors
Bitcoin is a digital currency designed to rely on a decentralized, trustless network of anonymous agents. Using a pseudonymous-address-linking procedure that achieves >99% sensitivity and >99% specificity, we reveal that between launch (January 3rd, 2009), and when the price reached $1 (February 9th, 2011), most bitcoin was mined by only sixty-four agents. This was due to the rapid emergence of Pareto distributions in bitcoin income, producing such extensive resource centralization that almost all contemporary bitcoin addresses can be connected to these top agents by a chain of six transactions. Centralization created a social dilemma. Attackers could routinely exploit bitcoin via a "51% attack", making it possible for them to repeatedly spend the same bitcoins. Yet doing so would harm the community. Strikingly, we find that potential attackers always chose to cooperate instead. We model this dilemma using an N-player Centipede game in which anonymous players can choose to exploit, and thereby undermine, an appreciating good. Combining theory and economic experiments, we show that, even when individual payoffs are unchanged, cooperation is more frequent when the game is played by an anonymous group. Although bitcoin was designed to rely on a decentralized, trustless network of anonymous agents, its early success rested instead on cooperation among a small group of altruistic founders.
Georgios Amanatidis, Georgios Birmpas, Philip Lazos, Francisco J. Marmolejo-CossÃo
Motivated by governance models adopted in blockchain applications, we study the problem of selecting appropriate system updates in a decentralised way. Contrary to most existing voting approaches, we use the input of a set of motivated experts of varying levels of expertise. In particular, we develop an approval voting inspired selection mechanism through which the experts approve or disapprove the different updates according to their perception of the quality of each alternative. Given their opinions, and weighted by their expertise level, a single update is then implemented and evaluated, and the experts receive rewards based on their choices. We show that this mechanism always has approximate pure Nash equilibria and that these achieve a constant factor approximation with respect to the quality benchmark of the optimal alternative. Finally, we study the repeated version of the problem, where the weights of the experts are adjusted after each update, according to their performance. Under mild assumptions about the weights, the extension of our mechanism still has approximate pure Nash equilibria in this setting.
Weizhao Tang, Lucianna Kiffer, Giulia Fanti, Ari Juels
Most permissionless blockchain networks run on peer-to-peer (P2P) networks, which offer flexibility and decentralization at the expense of performance (e.g., network latency). Historically, this tradeoff has not been a bottleneck for most blockchains. However, an emerging host of blockchain-based applications (e.g., decentralized finance) are increasingly sensitive to latency; users who can reduce their network latency relative to other users can accrue (sometimes significant) financial gains. In this work, we initiate the study of strategic latency reduction in blockchain P2P networks. We first define two classes of latency that are of interest in blockchain applications. We then show empirically that a strategic agent who controls only their local peering decisions can manipulate both types of latency, achieving 60\% of the global latency gains provided by the centralized, paid service bloXroute, or, in targeted scenarios, comparable gains. Finally, we show that our results are not due to the poor design of existing P2P networks. Under a simple network model, we theoretically prove that an adversary can always manipulate the P2P network's latency to their advantage, provided the network experiences sufficient peer churn and transaction activity.
Hongze Liu, Jie Li, Shijing Yuan, Wenqi Cao · 5 authors
Hierarchical Federated Learning (HFL) is introduced as a promising technique that allows model owners to fully exploit computational resources and bandwidth resources to train the global model. However, due to the high training cost, a single model owner may not be able to deploy HFL. To address this issue, we develop a smart contract based trust crowdfunding mechanism for HFL, which enables multiple model owners to obtain a crowdfunding model with high social utility for multiple crowdfunding participants. To ensure the authenticity of the crowdfunding mechanism, we implemented the Vickey-Clark-Croves (VCG) mechanism to encourage all crowdfunding participants and clients to provide realistic bids and offers. At the same time, in order to ensure guaranteed trustworthiness of crowdfunding and automatic distribution of funds, we develop and implement a smart contract to record the crowdfunding process and training results in the blockchain. We prove that the proposed scheme satisfies the budget balance and participant constraint. Finally, we implement a prototype of this smart contract on an Ethereoum private chain and evaluate the proposed VCG mechanism. The experimental results demonstrate that the proposed scheme can effectively improve social utility while ensuring the authenticity and trustworthiness of the crowdfunding process.
An option is a financial agreement between two parties to trade two assets. One party is given the right, but not the obligation, to complete the swap before a specified termination time. In todays financial markets, an option is considered an asset which can itself be transferred: while an option is active, one party can sell its rights (or obligations) to another. Todays blockchains support simple options in the form of cross-chain atomic swap protocols where one party has the choice whether to complete the swap. The options implemented by these cross-chain protocols, are not, however, transferable. This paper proposes novel distributed protocols for transferable cross-chain options, where both option owners and providers can sell their positions to third parties. The protocol ensures that none of the parties can be cheated, that no unauthorized party can interfere, and that the transfer succeeds if the buyer and seller faithfully follow the protocol.
We present a decentralized online gaming platform implemented as a Decentralized Application (DApp) on the Ethereum blockchain. The gaming platform enables secure gaming, where the account balances and the stakes of the players are secured by a smart contract. Moreover, the fair enforcement of the game rules and the deposit of the winnings of the players and the gaming platform into their accounts are guaranteed by the smart contract. The gaming platform proposes lending functionalities that allow players to securely borrow tokens from the gaming platform in order to participate in the games.
In this study, we propose PRETRUST, a new framework to address the problem of the efficiency of payment process based on blockchain systems. PRETRUST is based on the thoughts of consortium chains, supporting fast payments. To make parties address transactions efficiently and scale out the capacity, the main strategy is based on the guarantee mechanism and shard technology with a random arrangement of transactions and consensus groups. In PRETRUST, the guarantee process replaces the verification of a transaction on-chain to realize the optimization of efficiency. And the model of a trusted execution environment is utilized in the interactions between PRETRUST and public systems. Throughout the paper, we discuss several transaction scenarios and analyze the security, which shows that PRETRUST can guarantee security.