Bias in Federated Learning: Factors, Effects, Mitigations, and Open Issues

Abstract

Federated learning (FL) enables collaborative model training from decentralized data while preserving privacy. However, biases manifest due to sample selection, population drift, locally biased data, societal issues, algorithmic assumptions, and representation choices. These biases accumulate in FL models, causing unfairness. Tailored detection and mitigation methods are needed. This paper analyzes sources of bias unique to FL, their effects, and specialized mitigation strategies like robust aggregation, cryptographic protocols, and algorithmic debiasing. We categorize techniques and discuss open challenges around miscoordination, privacy constraints, decentralized evaluation, data poisoning attacks, systems heterogeneity, incentive misalignments, personalization tradeoffs, emerging governance needs, and participation. As FL expands into critical domains, ensuring equitable access without ingrained biases is imperative. This study provides a conceptual foundation for future research on developing accurate, robust and fair FL through tailored technical solutions and participatory approaches attuned to the decentralized environment. It aims to motivate further work toward trustworthy and inclusive FL.