OFL-W3: A One-shot Federated Learning System on Web 3.0
Linshan Jiang, Moming Duan, Bingsheng He, Yulin Sun, Peishen Yan, Yang Hua, Tao Song
TL;DR
This paper tackles data silos and privacy constraints by proposing OFL-W3, a one-shot federated learning system designed for Web 3.0. It integrates smart contracts for tokenized transactions, IPFS for decentralized model sharing, and PFNM with Leave-one-out incentives to perform aggregation and payments in a single communication round. The approach is demonstrated on Sepolia with a PFNM-based aggregation of MNIST models, demonstrating a global accuracy of 93.87% and outlining the associated gas costs and overhead. The work showcases a practical blueprint for deploying privacy-preserving, incentive-driven learning in Web 3.0 environments and suggests directions for AI+Web3 research and deployment.
Abstract
Federated Learning (FL) addresses the challenges posed by data silos, which arise from privacy, security regulations, and ownership concerns. Despite these barriers, FL enables these isolated data repositories to participate in collaborative learning without compromising privacy or security. Concurrently, the advancement of blockchain technology and decentralized applications (DApps) within Web 3.0 heralds a new era of transformative possibilities in web development. As such, incorporating FL into Web 3.0 paves the path for overcoming the limitations of data silos through collaborative learning. However, given the transaction speed constraints of core blockchains such as Ethereum (ETH) and the latency in smart contracts, employing one-shot FL, which minimizes client-server interactions in traditional FL to a single exchange, is considered more apt for Web 3.0 environments. This paper presents a practical one-shot FL system for Web 3.0, termed OFL-W3. OFL-W3 capitalizes on blockchain technology by utilizing smart contracts for managing transactions. Meanwhile, OFL-W3 utilizes the Inter-Planetary File System (IPFS) coupled with Flask communication, to facilitate backend server operations to use existing one-shot FL algorithms. With the integration of the incentive mechanism, OFL-W3 showcases an effective implementation of one-shot FL on Web 3.0, offering valuable insights and future directions for AI combined with Web 3.0 studies.