Clients Eligibility-Based Lightweight Protocol in Federated Learning: An IDS Use Case

Federated learning (FL) enables clients to train models locally, enhancing privacy by avoiding data centralization. Traditional FL assumes all clients have adequate resources, an often unrealistic expectation in heterogeneous networks with resource constraints like limited battery, memory, and bandw...

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Veröffentlicht in:	IEEE eTransactions on network and service management 2024-08, Vol.21 (4), p.3759-3774
Hauptverfasser:	Asad, Muhammad, Otoum, Safa, Shaukat, Saima
Format:	Artikel
Sprache:	eng
Schlagworte:	client eligibility Convergence Data models Federated learning heterogeneous network Internet of Things intrusion detection Optimization Protocols resource-constrained Servers Training
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