MAP: Model Aggregation and Personalization in Federated Learning with Incomplete Classes

arXiv:2404.09232v1 Announce Type: new
Abstract: In some real-world applications, data samples are usually distributed on local devices, where federated learning (FL) techniques are proposed to coordinate decentralized clients without directly sharing users' private data. FL commonly follows the parameter server architecture and contains multiple personalization and aggregation procedures. The natural data heterogeneity across clients, i.e., Non-I.I.D. data, challenges both the aggregation and personalization goals in FL. In this paper, we focus on a special kind of Non-I.I.D. scene where clients …

abstract aggregation applications architecture arxiv cs.dc cs.lg data decentralized devices distributed federated learning map multiple personalization private data samples server type world