Shift Happens: Mixture of Experts based Continual Adaptation in Federated Learning

• URL: http://arxiv.org/abs/2506.18789v1
• Date: Mon, 23 Jun 2025 15:59:21 GMT
• Title: Shift Happens: Mixture of Experts based Continual Adaptation in Federated Learning
• Authors: Rahul Atul Bhope, K. R. Jayaram, Praveen Venkateswaran, Nalini Venkatasubramanian,
• Abstract summary: Federated Learning (FL) enables collaborative model training across decentralized clients without sharing raw data.<n>We introduce ShiftEx, a shift-aware mixture of experts framework that creates and trains specialized global models in response to detected distribution shifts.<n>We demonstrate 5.5-12.9 percentage point accuracy improvements and 22-95 % faster adaptation compared to state-of-the-art FL baselines across diverse shift scenarios.
• Score: 3.120955853908236
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Federated Learning (FL) enables collaborative model training across decentralized clients without sharing raw data, yet faces significant challenges in real-world settings where client data distributions evolve dynamically over time. This paper tackles the critical problem of covariate and label shifts in streaming FL environments, where non-stationary data distributions degrade model performance and require adaptive middleware solutions. We introduce ShiftEx, a shift-aware mixture of experts framework that dynamically creates and trains specialized global models in response to detected distribution shifts using Maximum Mean Discrepancy for covariate shifts. The framework employs a latent memory mechanism for expert reuse and implements facility location-based optimization to jointly minimize covariate mismatch, expert creation costs, and label imbalance. Through theoretical analysis and comprehensive experiments on benchmark datasets, we demonstrate 5.5-12.9 percentage point accuracy improvements and 22-95 % faster adaptation compared to state-of-the-art FL baselines across diverse shift scenarios. The proposed approach offers a scalable, privacy-preserving middleware solution for FL systems operating in non-stationary, real-world conditions while minimizing communication and computational overhead.

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