Professor Ali Anwar at ECE Fall 2024 Colloquium

Towards resource-aware federated learning with interactive debugging solutions

This talk explores two pivotal advancements in Federated Learning (FL) systems aimed at addressing resource management, client reliability, and debugging challenges. First, I present FLOAT, a framework designed to tackle resource heterogeneity and performance inconsistencies in FL by dynamically optimizing client resource utilization, reducing dropouts, and enhancing model convergence. FLOAT leverages multi-objective Reinforcement Learning with Human Feedback to automate optimization selection based on client conditions, showing a significant boost in model accuracy and efficiency. Next, I introduce FedDebug, a fault localization framework that enables effective debugging in FL environments. By integrating record-and-replay techniques for real-time inspection and adapting differential testing with neuron activation analysis, FedDebug identifies clients causing performance degradation with high accuracy, all without additional testing data or labels.