Boosting Communication Efficiency of Federated Learning's Secure Aggregation

arXiv:2405.01144v1 Announce Type: new
Abstract: Federated Learning (FL) is a decentralized machine learning approach where client devices train models locally and send them to a server that performs aggregation to generate a global model. FL is vulnerable to model inversion attacks, where the server can infer sensitive client data from trained models. Google's Secure Aggregation (SecAgg) protocol addresses this data privacy issue by masking each client's trained model using shared secrets and individual elements generated locally on the client's device. …

aggregation arxiv attacks can client communication cs.cr data decentralized devices efficiency federated federated learning global locally machine machine learning send sensitive server train vulnerable