Blockchain Papers

B M Roy, Peter Potash, Marcos Villagra

Low-Rank Adaptation (LoRA) is a widely adopted method for customizing large-scale language models. In distributed, untrusted training environments, an open source base model user may want to use LoRA weights created by an external contributor, leading to two requirements: (1) the base model user must confirm that the LoRA weights are effective when paired with the intended base model, and (2) the LoRA contributor must keep their proprietary weights private until compensation is assured. We present ZKLoRA, a zero-knowledge verification protocol that relies on succinct proofs and our novel Multi-Party Inference procedure to verify LoRA-base model compatibility without exposing LoRA weights. ZKLoRA produces deterministic correctness guarantees and validates each LoRA module in only 1-2 seconds on state-of-the-art large language models. This low-latency approach enables nearly real-time verification and promotes secure collaboration among geographically decentralized teams and contract-based training pipelines. The protocol ensures that the delivered LoRA module works as claimed, safeguarding the contributor's intellectual property while providing the base model user with verification of compatibility and lineage.

Kaya Alpturer, S. Matthew Weinberg

It is well-known that RANDAO manipulation is possible in Ethereum if an adversary controls the proposers assigned to the last slots in an epoch. We provide a methodology to compute, for any fraction $α$ of stake owned by an adversary, the maximum fraction $f(α)$ of rounds that a strategic adversary can propose. We further implement our methodology and compute $f(\cdot)$ for all $α$. For example, we conclude that an optimal strategic participant with $5\%$ of the stake can propose a $5.048\%$ fraction of rounds, $10\%$ of the stake can propose a $10.19\%$ fraction of rounds, and $20\%$ of the stake can propose a $20.68\%$ fraction of rounds.

S. S. Iyengar, Sanjeev Kaushik Ramani, Buke Ao

Information fusion has been a topic of immense interest owing to its applicability in various applications. This brings to the fore the need for a flexible and accurate fusion algorithm that can be versatile. The Brooks–Iyengar algorithm is one such fusion algorithm. It has since its inception found numerous applications that deal with the fusion of data from multiple sources. The uniqueness of the Brooks–Iyengar algorithm is the ease with which the data from multiple sensors in a local system can be fused and also reach consensus in a distributed system with the added capability of fault tolerance. Blockchain has found its use as a distributed ledger and has successfully supported and fueled many crypto-currencies over the years. Information fusion with regards to Blockchains is a topic of great research interest in the past couple of years. Since blockchain has no official node, the introduction of a decentralized network and a consensus algorithm is required in making the interactions and exchanges between multiple suppliers easier and thus leads to business being carried out without any hassles. In this paper, we attempt to understand and describe the deployment of multiple sensors to measure various aspects of the physical world. We discuss a novel technique of employing the Brooks–Iyengar algorithm in the design of the system that would decentralize the data source from the corresponding measurements and thus ensure the integrity of the transactions in the Blockchain. Finally, a theoretical analysis of the performance of the algorithm when used in a blockchain based decentralized environment is also discussed.

Sergey Edward Lyshevski

Distributed processing and control are critical to supports distributed intelligence and autonomy of multi-degree-of-freedom motion systems. Measurements and fusion of spatiotemporal physical quantities imply data-intensive computing and spatial distribution of computing resources to enable control and processing of large data sets from image and inertial sensors. We examine distributed and asynchronous processing nodes which process information independently deriving partial solutions. There are multiple sensing-and-processing nodes in each individual agent. Each node comprises solid-state or MEMS multi-degree-of-freedom sensors with ASICs which process and fuse data. On-node computing supports distributed processing. Adaptive bottom-up organization ensures data aggregation and data management with operation on sub-samples or hashed sets of large source datasets. Cooperative distributed processing is essential in centralized, decentralized and behavioral coordination. In the centralized organization, a central processor may not ensure adequacy. A network of semi-autonomous on-device processing sensors may interact to solve specific tasks and validate solutions. Problem allocation, partitioning, coordination and other tasks are implemented using software-and hardware-supported algorithms and protocols. This paper contributes to design of next generation of systems with distributed multi-node processing capabilities.

Hendrick C. Lambert, D. Sinno

We present an algorithm, inspired by self-organization and stigmergy observed in biological swarms, for managing multiple sensors tracking large numbers of targets. We devise a decentralized architecture wherein autonomous sensors manage their own data collection resources and task themselves. Sensors cannot communicate with each other directly; however, a global track file, which is continuously broadcast, allows the sensors to infer their contributions to the global estimation of target states. Sensors can transmit their data (either as raw measurements or some compressed format) only to a central processor where their data are combined to update the global track file. We outline information-theoretic rules for the general multiple-sensor Bayesian target tracking problem. We provide specific formulas for problems dominated by additive white Gaussian noise. Using Cramér-Rao lower bounds as surrogates for error covariances, we illustrate, using numerical scenarios involving ballistic targets, that the bioinspired algorithm is highly scalable and performs very well for large numbers of targets.

Gregor Pavlin, Patrick de Oude, Marinus Maris, Jan Nunnink · 5 authors

No abstract is available for this record.