Researchers created a Novel Framework called ‘FedD3’ for Federated Learning in Resource-Constrained Edge Environments via Decentralized Dataset Distillation

For collaborative learning in large-scale distributed systems with a sizable number of networked clients, such as smartphones, connected cars, or edge devices, federated learning has emerged as a paradigm. Previous research has attempted to speed up convergence, reduce the number of required operations, and increase communication efficiency due to the limited bandwidth between clients. However, this type of cooperative optimization still results in high communication volumes for current neural networks with over a billion parameters, necessitating significant network capacity (up …

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