FedDebug: Systematic Debugging for Federated Learning Applications

arXiv:2301.03553v2 Announce Type: replace-cross
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 …

abstract applications arxiv build challenges collaborative concerns cs.cv cs.dc cs.lg cs.se data debugging federated learning global imaging medical medical imaging privacy them train training type