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Dynamic Scheduling for Federated Edge Learning with Streaming Data
In this work, we consider a Federated Edge Learning (FEEL) system where training data are randomly generated over time at a set of distributed edge devices with long-term energy constraints.
Scheduling and Aggregation Design for Asynchronous Federated Learning over Wireless Networks
Federated Learning (FL) is a collaborative machine learning (ML) framework that combines on-device training and server-based aggregation to train a common ML model among distributed agents.
Device Scheduling and Update Aggregation Policies for Asynchronous Federated Learning
Federated Learning (FL) is a newly emerged decentralized machine learning (ML) framework that combines on-device local training with server-based model synchronization to train a centralized ML model over distributed nodes.
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