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Embedding Alignment for Unsupervised Federated Learning via Smart Data Exchange
In most of the current literature, FL has been studied for supervised ML tasks, in which edge devices collect labeled data.
Network-Aware Optimization of Distributed Learning for Fog Computing
Unlike traditional federated learning frameworks, our method enables devices to offload their data processing tasks to each other, with these decisions determined through a convex data transfer optimization problem that trades off costs associated with devices processing, offloading, and discarding data points.
Distributed, Parallel, and Cluster Computing
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