6 papers
Eigenspace-Based Clustering for Personalized System Identification
Abdulmoneam Ali, Dipankar Maity, Ahmed Arafa
We study the problem of system identification in heterogeneous settings, where different systems may follow distinct underlying dynamics. Existing clustered system identification a…
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…
Delay Sensitive Hierarchical Federated Learning with Stochastic Local Updates
Abdulmoneam Ali, Ahmed Arafa
The impact of local averaging on the performance of federated learning (FL) systems is studied in the presence of communication delay between the clients and the parameter server.…
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…