Gradient-Refined Federated Learning on Head-Tail Imbalanced Data
Summary
Federated learning (a training method where multiple computers learn together while keeping their data private) struggles when some classes have much more training data than others, a problem called head-tail imbalance. Researchers introduced FedGRE, a new approach that improves the shared learning signals (called gradients, which guide how the model updates) using two techniques: accumulation diffusion (mixing stored gradient information with random noise to balance classes) and accumulation refinement (using stored information as a reference point to keep updates stable). Tests on six datasets showed FedGRE outperformed 14 competing methods while protecting privacy.
Solution / Mitigation
The paper proposes FedGRE as a solution, which uses two mechanisms: (1) accumulation diffusion, which "amalgamates accumulated gradients with stochastic gradient perturbations to alleviate class imbalance," and (2) accumulation refinement, which "utilizes the accumulation as an anchor to calibrate global gradient updates, ensuring consistency and mitigating oscillations." The approach also implements "a consistency integration technique to incorporate the refined accumulation into the global model, guaranteeing privacy-preserving and class-balanced global optimization."
Classification
Original source: http://ieeexplore.ieee.org/document/11359612
First tracked: August 23, 2026 at 02:01 AM
Classified by LLM (prompt v3) · confidence: 85%