OCD-FL: Opportunistic Communication-Efficient Decentralized Federated Learning
The author proposes OCD-FL as a novel scheme for decentralized federated learning, focusing on communication efficiency and energy consumption reduction.
Decentralized Federated Learning with Blockchain for Secure Model Verification
The author argues that Blockchain-based Decentralized Federated Learning (BDFL) enhances model verification and trustworthiness in decentralized machine learning systems by leveraging a blockchain infrastructure.
OCD-FL: A Novel Communication-Efficient Peer Selection-based Decentralized Federated Learning
分散フェデレーテッドラーニングにおける効率的な通信を重視した新しいピア選択ベースのOCD-FLの提案とその効果的な実装。
Optimizing Decentralized Federated Learning in Energy and Latency Constrained Wireless Networks
The core message of this paper is to propose an adaptive decentralized federated learning (DFL) framework that optimizes the number of local training rounds across diverse devices with varying resource budgets, in order to enhance the model performance while considering energy and latency constraints.
Decentralized Federated Learning: A Comprehensive Survey and Perspective
Decentralized federated learning (DFL) is an emerging framework that eliminates the need for a central server, enabling direct communication and knowledge sharing among clients to improve privacy, efficiency, and resource utilization.
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