1 citations · 1 across the 1 of their papers we have counts for
2 papers
cs.NI2026
FedMaJ: Ordinal Meta-Aggregation for Federated Learning Using Majority-Judgment Voting
Driss Choukri, Essaid Sabir, Halima Elbiaze +1
Federated learning traditionally relies on a fixed aggregation rule despite shifting client state and wireless dynamics. We present FedMaJ, a framework that dynamically selects agg…
cs.LG2024★ 1 cited
A Green Multi-Attribute Client Selection for Over-The-Air Federated Learning: A Grey-Wolf-Optimizer Approach
Maryam Ben Driss, Essaid Sabir, Halima Elbiaze +2
Federated Learning (FL) has gained attention across various industries for its capability to train machine learning models without centralizing sensitive data. While this approach…