FUSE: Fairness-aware Update Screening against Byzantine Exploitation in $q$-Fair Federated Learning
Client-level fair federated learning, in the form of $q$-fair federated learning ($q$-FFL), agnostic federated learning, or tilted empirical risk minimization, has emerged as an attractive solution to equalize utility across heterogeneous clients.
ProofPaper ↗
Key points
- This paradigm grants high-loss clients greater relative influence, while a central server aggregates their loss-weighted updates into a single shared model.
- Yet, the Byzantine robustness of $q$-fair federated learning has been largely neglected, leaving the server unable to tell legitimate fairness-induced amplification from malicious update scaling.
- Our analysis under shared class-conditional label skew shows that fairness scaling preserves the nonnegative class-mixture structure of honest updates, while replacing their unit-sum constraint by a client-dependent radial scale.
- We then introduce FUSE, a structure-aware robust aggregator that uses a small trusted labeled dataset for coefficient recovery, scale and cone-consistency screening, and post-screening step-size control.
Sources (1)
- [1]FUSE: Fairness-aware Update Screening against Byzantine Exploitation in $q$-Fair Federated LearningarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 14, 02:29 PM
Client-level fair federated learning, in the form of $q$-fair federated learning ($q$-FFL), agnostic federated learning, or tilted empirical risk minimization, has emerged as an attractive solution to equalize utility across heterogeneous clients.
This paradigm grants high-loss clients greater relative influence, while a central server aggregates their loss-weighted updates into a single shared model.
Extractive summary: sentences quoted from the sources.