Federated Learning with Energy-Based Structured Probabilistic Inference

2026-06-29

AI Summary

This work addresses the suboptimality of fixed or heuristic aggregation weights in federated learning caused by heterogeneous client data distributions. To overcome this limitation, the authors propose a dynamic aggregation mechanism based on Conditional Random Fields (CRFs), which introduces structured probabilistic inference into the federated aggregation process for the first time. The approach employs an energy function to jointly model each client’s unary reliability and pairwise interactions among clients, enabling dynamic optimization of the aggregation weights for the global model. Evaluated under non-IID data settings, the proposed method significantly outperforms mainstream federated learning baselines, yielding notable improvements in both convergence speed and final model accuracy.

Abstract

Federated learning typically aggregates client updates using fixed or heuristic weighting rules, which can be suboptimal when clients have heterogeneous data and varying contributions to the global model. We propose a framework that refines client aggregation weights using Conditional Random Fields (CRFs). Our method defines unary potentials for individual clients and pairwise potentials for all client pairs, allowing the server to model both client-specific reliability and interactions between clients. The resulting CRF inference produces aggregation weights that enable better convergence of the global training objective. Experiments show that, under non-IID heterogeneity, our approach consistently improves performance over well-established federated learning baselines.

Problem

• Federated Learning
• Client Heterogeneity
• Non-IID Data
• Aggregation Weights
• Model Convergence

Innovation

• Federated Learning
• Conditional Random Fields
• Energy-Based Model
• Client Weighting
• Non-IID Heterogeneity