SAFER-FL: A ROBUST CLOUD–EDGE FEDERATED LEARNING FRAMEWORK FOR SECURE COMMUNICATION AND COMPUTATION UNDER POISONING ATTACKS

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The dataset proposes SAFER-FL, a strong and safe federated learning framework that solves the problems of secure communication and computation in cloud-edge settings that are under poisoning threats. The framework uses important tools, including accuracy-based client filtering, trimmed aggregation, and safe aggregation, to lessen the effects of bad client updates.

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