OFL-W3: A One-shot Federated Learning System on Web 3.0

TL;DR

OFL-W3 introduces a practical one-shot federated learning system tailored for Web 3.0, leveraging blockchain and IPFS to enable privacy-preserving collaborative AI with minimal client-server interactions.

Contribution

This work presents the first integrated system combining one-shot federated learning with blockchain and Web 3.0 technologies, addressing latency and transaction constraints.

Findings

Effective implementation of one-shot FL on Web 3.0

Utilizes smart contracts and IPFS for decentralized management

Incorporates incentive mechanisms for participant engagement

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.