A survey categorizing and comparing twelve federated learning methods for partial client participation, weakened by several citation mismatches and unsourced benchmark numbers.
Efficient Client Selection in Federated Learning
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abstract
Federated Learning (FL) enables decentralized machine learning while preserving data privacy. This paper proposes a novel client selection framework that integrates differential privacy and fault tolerance. The adaptive client selection adjusts the number of clients based on performance and system constraints, with noise added to protect privacy. Evaluated on the UNSW-NB15 and ROAD datasets for network anomaly detection, the method improves accuracy by 7% and reduces training time by 25% compared to baselines. Fault tolerance enhances robustness with minimal performance trade-offs.
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Overcoming Challenges of Partial Client Participation in Federated Learning : A Comprehensive Review
A survey categorizing and comparing twelve federated learning methods for partial client participation, weakened by several citation mismatches and unsourced benchmark numbers.