Mechanistic Analysis of Circuit Preservation in Federated Learning
This addresses the problem of accuracy loss in Federated Learning for decentralized data scenarios, providing mechanistic insights that could lead to more targeted solutions, though it is incremental in applying mechanistic interpretability to a known bottleneck.
The paper tackled the performance degradation of Federated Learning under Non-IID data by investigating the internal mechanistic causes, finding that conflicting client updates lead to circuit collapse and degradation in the global model, with evidence quantified using Intersection-over-Union (IoU).
Federated Learning (FL) enables collaborative training of models on decentralized data, but its performance degrades significantly under Non-IID (non-independent and identically distributed) data conditions. While this accuracy loss is well-documented, the internal mechanistic causes remain a black box. This paper investigates the canonical FedAvg algorithm through the lens of Mechanistic Interpretability (MI) to diagnose this failure mode. We hypothesize that the aggregation of conflicting client updates leads to circuit collapse, the destructive interference of functional, sparse sub-networks responsible for specific class predictions. By training inherently interpretable, weight-sparse neural networks within an FL framework, we identify and track these circuits across clients and communication rounds. Using Intersection-over-Union (IoU) to quantify circuit preservation, we provide the first mechanistic evidence that Non-IID data distributions cause structurally distinct local circuits to diverge, leading to their degradation in the global model. Our findings reframe the problem of statistical drift in FL as a concrete, observable failure of mechanistic preservation, paving the way for more targeted solutions.