Server-Side Debiasing Method EquFL Reduces Fairness Loss in Federated Learning

Global: Server-Side Debiasing Method EquFL Reduces Fairness Loss in Federated Learning

A new server‑side technique called EquFL has been introduced to address fairness concerns in federated learning systems. The method generates a calibrated update after the server collects client model updates, integrates this calibration with the aggregated updates, and thereby produces a global model with reduced bias while preserving the convergence properties of standard FedAvg.

Background on Federated Learning Fairness

Federated learning enables multiple clients to collaboratively train a shared model under a central coordinator without exchanging raw training data. Although this distributed paradigm protects data privacy, it often yields disparate performance across demographic groups, raising fairness challenges that can affect the reliability of deployed AI services.

Limitations of Existing Debiasing Techniques

Prior fairness‑aware approaches typically require modifications to clients’ local training procedures or impose rigid aggregation rules, limiting their flexibility and increasing implementation complexity for heterogeneous participants.

EquFL: Server‑Side Calibration Approach

EquFL operates entirely on the server side. After receiving model updates from clients, the server computes a single calibrated adjustment that is merged with the standard aggregated update. This calibrated step is designed to counteract identified biases before the global model is broadcast back to the clients.

Theoretical Guarantees

The authors provide proofs that EquFL converges to the same optimal global model as FedAvg under standard assumptions, while simultaneously achieving a measurable reduction in fairness loss across training rounds.

Experimental Findings

Empirical evaluations reported in the abstract demonstrate that EquFL significantly mitigates bias in benchmark federated learning scenarios, confirming its practical effectiveness compared to baseline methods.

Implications for Deployment

Because EquFL requires no changes to client‑side code, it offers a scalable solution for organizations seeking to improve fairness in federated deployments without disrupting existing training pipelines.

This report is based on information from arXiv, licensed under Academic Preprint / Open Access. Based on the abstract of the research paper. Full text available via ArXiv.