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Towards Flexible Device Participation in Federated Learning
Traditional federated learning algorithms impose strict requirements on the participation rates of devices, which limit the potential reach of federated learning.
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
FedSoft: Soft Clustered Federated Learning with Proximal Local Updating
Traditionally, clustered federated learning groups clients with the same data distribution into a cluster, so that every client is uniquely associated with one data distribution and helps train a model for this distribution.
Fair Concurrent Training of Multiple Models in Federated Learning
We show how our fairness-based learning and incentive mechanisms impact training convergence and finally evaluate our algorithm with multiple sets of learning tasks on real world datasets.
