Stragglers Can Contribute More: Uncertainty-Aware Distillation for Asynchronous Federated Learning

2025-11-25

AI Summary

Asynchronous federated learning (FL) faces two key challenges: outdated model updates from straggler clients degrade global model performance, while fast clients exacerbate bias under data heterogeneity. Existing approaches typically address only one challenge, failing to balance both. This paper proposes FedEcho, the first asynchronous FL framework to incorporate uncertainty estimation. FedEcho employs uncertainty-aware knowledge distillation to dynamically assess the reliability of each client’s local predictions and performs reliability-guided, dynamically weighted model aggregation. Crucially, it operates without accessing private client data. By downweighting unreliable or stale updates, FedEcho simultaneously mitigates the adverse impact of staleness and alleviates bias induced by statistical heterogeneity. Extensive experiments demonstrate that FedEcho significantly outperforms state-of-the-art asynchronous FL methods under high network latency and strong non-IID data distributions, achieving superior robustness and communication efficiency.

Technology Category

• Machine Learning: Distributed Machine Learning & Federated Learning
• Reasoning under Uncertainty: Stochastic Optimization
• Multiagent Systems: Multiagent Systems under Uncertainty

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
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Asynchronous federated learning (FL) has recently gained attention for its enhanced efficiency and scalability, enabling local clients to send model updates to the server at their own pace without waiting for slower participants. However, such a design encounters significant challenges, such as the risk of outdated updates from straggler clients degrading the overall model performance and the potential bias introduced by faster clients dominating the learning process, especially under heterogeneous data distributions. Existing methods typically address only one of these issues, creating a conflict where mitigating the impact of outdated updates can exacerbate the bias created by faster clients, and vice versa. To address these challenges, we propose FedEcho, a novel framework that incorporates uncertainty-aware distillation to enhance the asynchronous FL performances under large asynchronous delays and data heterogeneity. Specifically, uncertainty-aware distillation enables the server to assess the reliability of predictions made by straggler clients, dynamically adjusting the influence of these predictions based on their estimated uncertainty. By prioritizing more certain predictions while still leveraging the diverse information from all clients, FedEcho effectively mitigates the negative impacts of outdated updates and data heterogeneity. Through extensive experiments, we demonstrate that FedEcho consistently outperforms existing asynchronous federated learning baselines, achieving robust performance without requiring access to private client data.

Problem

• Addresses outdated updates from stragglers degrading model performance
• Mitigates bias from faster clients dominating heterogeneous data learning
• Resolves conflict between handling stragglers and faster client dominance

Innovation

• Uncertainty-aware distillation assesses straggler prediction reliability
• Dynamic adjustment of influence based on estimated uncertainty
• Prioritizes certain predictions while leveraging diverse client information