UnifyFL: Enabling Decentralized Cross-Silo Federated Learning
Sarang S, Druva Dhakshinamoorthy, Aditya Shiva Sharma, Yuvraj Singh Bhadauria, Siddharth Chaitra Vivek, Arihant Bansal, Arnab K. Paul
TL;DR
UnifyFL tackles the challenge of enabling trustworthy and resource-efficient collaboration across diverse FL clusters by introducing a decentralized cross-silo FL framework. It combines a decentralized orchestrator on a private Ethereum network with IPFS-based distributed storage and a Flower-based training workflow, plus randomized scorers to audibly validate models, supporting both synchronous and asynchronous operation. The paper details the architecture, implementation, and a thorough evaluation on real testbeds, showing convergence and performance close to ideal multilevel FL with significantly reduced overhead. The work demonstrates practical potential for scalable, trustful inter-organizational FL and outlines future work on multi-model FL, Byzantine defense, and privacy-preserving enhancements.
Abstract
Federated Learning (FL) is a decentralized machine learning (ML) paradigm in which models are trained on private data across several devices called clients and combined at a single node called an aggregator rather than aggregating the data itself. Many organizations employ FL to have better privacy-aware ML-driven decision-making capabilities. However, organizations often operate independently rather than collaborate to enhance their FL capabilities due to the lack of an effective mechanism for collaboration. The challenge lies in balancing trust and resource efficiency. One approach relies on trusting a third-party aggregator to consolidate models from all organizations (multilevel FL), but this requires trusting an entity that may be biased or unreliable. Alternatively, organizations can bypass a third party by sharing their local models directly, which requires significant computational resources for validation. Both approaches reflect a fundamental trade-off between trust and resource constraints, with neither offering an ideal solution. In this work, we develop a trust-based cross-silo FL framework called UnifyFL, which uses decentralized orchestration and distributed storage. UnifyFL provides flexibility to the participating organizations and presents synchronous and asynchronous modes to handle stragglers. Our evaluation on a diverse testbed shows that UnifyFL achieves a performance comparable to the ideal multilevel centralized FL while allowing trust and optimal use of resources.