Training Heterogeneous Client Models using Knowledge Distillation in Serverless Federated Learning

Federated Learning (FL) is an emerging machine learning paradigm that enables the collaborative training of a shared global model across distributed clients while keeping the data decentralized. Recent works on designing systems for efficient FL have shown that utilizing serverless computing technologies, particularly Function-as-a-Service (FaaS) for FL, can enhance resource efficiency, reduce training costs, and alleviate the complex infrastructure management burden on data holders. However, existing serverless FL systems implicitly assume a uniform global model architecture across all participating clients …

as-a-service client collaborative computing cs.ai cs.dc cs.lg data decentralized designing distillation distributed federated learning function global knowledge machine machine learning paradigm serverless serverless computing service systems technologies training