Convergence Visualizer of Decentralized Federated Distillation with Reduced Communication Costs
Author:
Affiliation:
1. Institute of Industorial Science, The University of Tokyo,Tokyo,JAPAN
2. Center for Spatial Information Science, The University of Tokyo,Chiba,JAPAN
Funder
NICT
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10436708/10436716/10437801.pdf?arnumber=10437801
Reference25 articles.
1. Communication-efficient learning of deep networks from decentralized data;McMahan
2. Advances and Open Problems in Federated Learning
3. IoT for 5G/B5G Applications in Smart Homes, Smart Cities, Wearables and Connected Cars
4. Decentralized Federated Learning: Fundamentals, State of the Art, Frameworks, Trends, and Challenges
5. Federated Learning With Cooperating Devices: A Consensus Approach for Massive IoT Networks
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