Beyond Aggregation: Guiding Clients in Heterogeneous Federated Learning

2025-09-26

AI Summary

To address performance degradation and inefficient task allocation caused by statistical heterogeneity in federated learning (FL), this paper proposes an active guidance paradigm: the central server not only aggregates model updates but also dynamically routes new tasks—e.g., patient triage—to the most suitable clients based on their local data distributions. Methodologically, we introduce the first empirical-likelihood-driven joint optimization framework that simultaneously learns local models and client-task matching policies. Unlike conventional passive aggregation, our approach achieves significant improvements on benchmark medical datasets: +3.2–5.8% in global model accuracy and +12.4–18.7% in task-matching precision. By unifying model learning with resource-aware scheduling, the method enables synergistic optimization of both statistical and system efficiency. This paradigm offers a novel pathway toward intelligent, privacy-preserving FL systems—particularly valuable in sensitive domains such as healthcare.

Technology Category

• Machine Learning: Distributed Machine Learning & Federated Learning
• Search and Optimization: Learning to Search
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Systems and Infrastructure for Web, Mobile and WoT: Federated Web and WoT systems, including distributed, federated and edge-based data processing

Abstract

Federated learning (FL) is increasingly adopted in domains like healthcare, where data privacy is paramount. A fundamental challenge in these systems is statistical heterogeneity-the fact that data distributions vary significantly across clients (e.g., different hospitals may treat distinct patient demographics). While current FL algorithms focus on aggregating model updates from these heterogeneous clients, the potential of the central server remains under-explored. This paper is motivated by a healthcare scenario: could a central server not only build a model but also guide a new patient to the hospital best equipped for their specific condition? We generalize this idea to propose a novel paradigm for FL systems where the server actively guides the allocation of new tasks or queries to the most appropriate client in the network. To enable this, we introduce an empirical likelihood-based framework that simultaneously addresses two goals: (1) learning effective local models on each client, and (2) finding the best matching client for a new query. Empirical results demonstrate the framework's effectiveness on benchmark datasets, showing improvements in both model accuracy and the precision of client guidance compared to standard FL approaches. This work opens a new direction for building more intelligent and resource-efficient federated systems that leverage heterogeneity as a feature, not just a bug. Code is available at https://github.com/zijianwang0510/FedDRM.git.

Problem

• Guiding new tasks to appropriate clients in federated learning systems
• Addressing statistical heterogeneity across clients with different data distributions
• Simultaneously learning local models and matching queries to best clients

Innovation

• Server guides new queries to optimal clients
• Uses likelihood framework for client-task matching
• Simultaneously learns models and optimizes client allocation