The Other Side of the Coin: Unveiling the Downsides of Model Aggregation in Federated Learning from a Layer-peeled Perspective

TL;DR

This paper uncovers how model aggregation in federated learning causes cumulative feature degradation across layers, impairing performance, and analyzes why existing solutions mitigate this issue by preserving feature quality.

Contribution

It introduces a layer-peeled analysis framework revealing the root causes of performance drops in federated learning due to feature degradation during aggregation.

Findings

Aggregation degrades feature quality and coupling between features and layers.

Feature degradation accumulates with network depth, termed Cumulative Feature Degradation (CFD).

Existing solutions mitigate performance drop by reducing feature degradation.

Abstract

It is often observed that the aggregated model in FL underperforms on local data until after several rounds of local training. This temporary performance drop can potentially slow down the convergence of the FL model. Prior work regards this performance drop as an inherent cost of knowledge sharing among clients and does not give it special attention. While some studies directly focus on designing techniques to alleviate the issue, its root causes remain poorly understood. To bridge this gap, we construct a framework that enables layer-peeled analysis of how feature representations evolve during model aggregation in FL. It focuses on two key aspects: (1) the intrinsic quality of extracted features, and (2) the alignment between features and their subsequent parameters -- both of which are critical to downstream performance. Using this framework, we first investigate how model aggregation affects internal feature extraction process. Our analysis reveals that aggregation degrades feature quality and weakens the coupling between intermediate features and subsequent layers, both of which are well shaped during local training. More importantly, this degradation is not confined to specific layers but progressively accumulates with network depth -- a phenomenon we term Cumulative Feature Degradation (CFD). CFD significantly impairs the quality of penultimate-layer features and weakens their coupling with the classifier, ultimately degrading model performance. We further revisit several widely adopted solutions through the lens of layer-peeled feature extraction to understand why they are effective in addressing aggregation-induced performance drop. Our results show that their effectiveness lies in mitigating the feature degradation described above, which is well aligned with our observations.