MultiConfederated Learning: Inclusive Non-IID Data handling with Decentralized Federated Learning

arXiv:2404.13421v1 Announce Type: new
Abstract: Federated Learning (FL) has emerged as a prominent privacy-preserving technique for enabling use cases like confidential clinical machine learning. FL operates by aggregating models trained by remote devices which owns the data. Thus, FL enables the training of powerful global models using crowd-sourced data from a large number of learners, without compromising their privacy. However, the aggregating server is a single point of failure when generating the global model. Moreover, the performance of the model …

abstract arxiv cases clinical cs.ai cs.lg data decentralized devices enabling federated learning global machine machine learning privacy training type use cases