Efficient Client Selection in Federated Learning
This work addresses efficient and robust client selection for federated learning in privacy-sensitive applications like network anomaly detection, but it is incremental as it builds on existing FL methods with specific enhancements.
The paper tackles the problem of client selection in federated learning by proposing a framework that integrates differential privacy and fault tolerance, resulting in a 7% accuracy improvement and 25% reduction in training time on network anomaly detection datasets.
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.