Benchmarking federated strategies in Peer-to-Peer Federated learning for biomedical data

arXiv:2402.10135v1 Announce Type: new
Abstract: The increasing requirements for data protection and privacy has attracted a huge research interest on distributed artificial intelligence and specifically on federated learning, an emerging machine learning approach that allows the construction of a model between several participants who hold their own private data. In the initial proposal of federated learning the architecture was centralised and the aggregation was done with federated averaging, meaning that a central server will orchestrate the federation using the most …

abstract artificial artificial intelligence arxiv benchmarking biomedical construction cs.ai cs.dc cs.lg data data protection distributed federated learning intelligence machine machine learning peer peer-to-peer privacy private data protection requirements research strategies type