Papers › TraceFL: Interpretability-Driven Debugging in Federated Learning via Neuron Provenance

TraceFL: Interpretability-Driven Debugging in Federated Learning via Neuron Provenance

21 Dec 2023arXiv:2312.13632archive 2025-07-28

Waris Gill, Ali Anwar, Muhammad Ali Gulzar

In Federated Learning, clients train models on local data and send updates to a central server, which aggregates them into a global model using a fusion algorithm. This collaborative yet privacy-preserving training comes at a cost. FL developers face significant challenges in attributing global model predictions to specific clients. Localizing responsible clients is a crucial step towards (a) excluding clients primarily responsible for incorrect predictions and (b) encouraging clients who contributed high-quality models to continue participating in the future. Existing ML debugging approaches are inherently inapplicable as they are designed for single-model, centralized training. We introduce TraceFL, a fine-grained neuron provenance capturing mechanism that identifies clients responsible for a global model's prediction by tracking the flow of information from individual clients to the global model. Since inference on different inputs activates a different set of neurons of the global model, TraceFL dynamically quantifies the significance of the global model's neurons in a given prediction, identifying the most crucial neurons in the global model. It then maps them to the corresponding neurons in every participating client to determine each client's contribution, ultimately localizing the responsible client. We evaluate TraceFL on six datasets, including two real-world medical imaging datasets and four neural networks, including advanced models such as GPT. TraceFL achieves 99% accuracy in localizing the responsible client in FL tasks spanning both image and text classification tasks. At a time when state-of-the-artML debugging approaches are mostly domain-specific (e.g., image classification only), TraceFL is the first technique to enable highly accurate automated reasoning across a wide range of FL applications.

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seed-vt/tracefl officialmentioned in papermentioned on GitHubpytorch report

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Tasks

Explainable Models Fault localization Federated Learning Image Classification Interpretability Techniques for Deep Learning Interpretable Machine Learning Privacy Preserving Text Classification image-classification text-classification

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Adam Attention Attention Dropout BPE Cosine Annealing Dense Connections Discriminative Fine-Tuning Dropout GPT Layer Normalization Linear Layer Linear Warmup With Cosine Annealing Multi-Head Attention Residual Connection SET Softmax Weight Decay

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