A Reinforcement Learning-Based Approach to Graph Discovery in D2D-Enabled Federated Learning

no code implementations • 7 Aug 2023 • Satyavrat Wagle, Anindya Bijoy Das, David J. Love, Christopher G. Brinton

Augmenting federated learning (FL) with direct device-to-device (D2D) communications can help improve convergence speed and reduce model bias through rapid local information exchange.

Federated Learning Reinforcement Learning (RL)

Embedding Alignment for Unsupervised Federated Learning via Smart Data Exchange

no code implementations • 4 Aug 2022 • Satyavrat Wagle, Seyyedali Hosseinalipour, Naji Khosravan, Mung Chiang, Christopher G. Brinton

In most of the current literature, FL has been studied for supervised ML tasks, in which edge devices collect labeled data.

Contrastive Learning Federated Learning

Network-Aware Optimization of Distributed Learning for Fog Computing

no code implementations • 17 Apr 2020 • Yuwei Tu, Yichen Ruan, Su Wang, Satyavrat Wagle, Christopher G. Brinton, Carlee Joe-Wong

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