1 citations · 1 across the 6 of their papers we have counts for
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FedSIR: Spectral Client Identification and Relabeling for Federated Learning with Noisy Labels
Sina Gholami, Abdulmoneam Ali, Tania Haghighi +2
Federated learning (FL) enables collaborative model training without sharing raw data; however, the presence of noisy labels across distributed clients can severely degrade the lea…
FB-NLL: A Feature-Based Approach to Tackle Noisy Labels in Personalized Federated Learning
Abdulmoneam Ali, Ahmed Arafa
Personalized Federated Learning (PFL) aims to learn multiple task-specific models rather than a single global model across heterogeneous data distributions. Existing PFL approaches…
RCC-PFL: Robust Client Clustering under Noisy Labels in Personalized Federated Learning
Abdulmoneam Ali, Ahmed Arafa
We address the problem of cluster identity estimation in a personalized federated learning (PFL) setting in which users aim to learn different personal models. The backbone of effe…
Data Similarity-Based One-Shot Clustering for Multi-Task Hierarchical Federated Learning
Abdulmoneam Ali, Ahmed Arafa
We address the problem of cluster identity estimation in a hierarchical federated learning setting in which users work toward learning different tasks. To overcome the challenge of…