mlsanityData to 7 Oct 2026

FedDebug: Systematic Debugging for Federated Learning Applications

arXiv 2301.03553 ↗First posted 9 Jan 2023cs.SE cs.CV cs.DCcited by 6

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Abstract (arXiv)

In Federated Learning (FL), clients independently train local models and share them with a central aggregator to build a global model. Impermissibility to access clients' data and collaborative training make FL appealing for applications with data-privacy concerns, such as medical imaging. However, these FL characteristics pose unprecedented challenges for debugging. When a global model's performance deteriorates, identifying the responsible rounds and clients is a major pain point. Developers resort to trial-and-error debugging with subsets of clients, hoping to increase the global model's accuracy or let future FL rounds retune the model, which are time-consuming and costly. We design a systematic fault localization framework, FedDebug, that advances the FL debugging on two novel fronts. First, FedDebug enables interactive debugging of realtime collaborative training in FL by leveraging record and replay techniques to construct a simulation that mirrors live FL. FedDebug's breakpoint can help inspect an FL state (round, client, and global model) and move between rounds and clients' models seamlessly, enabling a fine-grained step-by-step inspection. Second, FedDebug automatically identifies the client(s) responsible for lowering the global model's performance without any testing data and labels--both are essential for existing debugging techniques. FedDebug's strengths come from adapting differential testing in conjunction with neuron activations to determine the client(s) deviating from normal behavior. FedDebug achieves 100% accuracy in finding a single faulty client and 90.3% accuracy in finding multiple faulty clients. FedDebug's interactive debugging incurs 1.2% overhead during training, while it localizes a faulty client in only 2.1% of a round's training time.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 56 of its 83 references that are in the map. First posted on arXiv 9 Jan 2023.

Builds on

56 of 83 references.

References · 56

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38 more
  1. 2018How To Backdoor Federated Learningcited by 745
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50 most cited of 56 All 56 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 3; 2024: 1; 2025: 1; 2026: 1.

6 papers.

  1. 2024MeanCache: User-Centric Semantic Caching for LLM Web Servicescited by 14
  2. 2023FedDefender: Backdoor Attack Defense in Federated Learningcited by 4
  3. 2023A Framework for Incentivized Collaborative Learningcited by 2
  4. 2023TraceFL: Interpretability-Driven Debugging in Federated Learning via Neuron Provenancecited by 1
  5. 2025FLStore: Efficient Federated Learning Storage for non-training workloadscited by 0
  6. 2026ProToken: Token-Level Attribution for Federated Large Language Modelscited by 0