FedDebug: Systematic Debugging for Federated Learning Applications

Waris Gill, Ali Anwar, Muhammad Ali Gulzar

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Scopus citations

Abstract

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, Fedde-bug,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'sbreakpoint can help inspect an FL state (round, client, and global model) and move between rounds and clients' models seam-lessly, 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 de-bugging incurs 1.2% overhead during training, while it localizes a faulty client in only 2.1% of a round's training time. With FedDebug,we bring effective debugging practices to federated learning, improving the quality and productivity of FL application developers.

Original languageEnglish (US)
Title of host publicationProceedings - 2023 IEEE/ACM 45th International Conference on Software Engineering, ICSE 2023
PublisherIEEE Computer Society
Pages512-523
Number of pages12
ISBN (Electronic)9781665457019
DOIs
StatePublished - 2023
Event45th IEEE/ACM International Conference on Software Engineering, ICSE 2023 - Melbourne, Australia
Duration: May 15 2023May 16 2023

Publication series

NameProceedings - International Conference on Software Engineering
ISSN (Print)0270-5257

Conference

Conference45th IEEE/ACM International Conference on Software Engineering, ICSE 2023
Country/TerritoryAustralia
CityMelbourne
Period5/15/235/16/23

Bibliographical note

Publisher Copyright:
© 2023 IEEE.

Keywords

  • CNN
  • client
  • fault localization
  • federated learning
  • neural networks
  • software debugging
  • testing

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