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Decentralized Federated Learning for UAV Networks: Architecture, Challenges, and Opportunities

IEEE NETWORK (2021)

Accelerating DNN Training in Wireless Federated Edge Learning Systems

Jinke Ren et al.

Summary: Training tasks in classical machine learning models are usually performed at remote cloud centers, which can be time-consuming and resource-heavy, posing privacy and communication latency issues. To address this, federated edge learning framework aggregates local learning updates at network edge, aiming to accelerate the training process.

IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS (2021)

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Article Engineering, Electrical & Electronic

A Joint Learning and Communications Framework for Federated Learning Over Wireless Networks

Mingzhe Chen et al.

Summary: This article discusses the challenges of training federated learning algorithms over a realistic wireless network and proposes an optimization model to minimize the FL loss function, providing a method to improve identification accuracy.

IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS (2021)

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Article Engineering, Electrical & Electronic