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Dynamic D2D-Assisted Federated Learning over O-RAN: Performance Analysis, MAC Scheduler, and Asymmetric User Selection
In this paper, we incorporate multi-granular system dynamics (MSDs) into FL, including (M1) dynamic wireless channel capacity, captured by a set of discrete-time events, called $\mathscr{D}$-Events, and (M2) dynamic datasets of users.
Synergies Between Federated Learning and O-RAN: Towards an Elastic Virtualized Architecture for Multiple Distributed Machine Learning Services
Motivated by these challenges, we introduce a generic FL paradigm over NextG networks, called dynamic multi-service FL (DMS-FL).
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