Coalitional Federated Learning: Improving Communication and Training on Non-IID Data With Selfish Clients-Reference-Cited by-同舟云学术

Coalitional Federated Learning: Improving Communication and Training on Non-IID Data With Selfish Clients

Author:

Arisdakessian Sarhad¹,Wahab Omar Abdel¹^ORCID,Mourad Azzam²^ORCID,Otrok Hadi³^ORCID

Affiliation:

1. Department of Computer Engineering and Software Engineering, Polytechnique Montréal, Montréal, QC, Canada

2. Department of Computer Science and Mathematics, Lebanese American University, Beirut, Lebanon

3. Department of EECS, Khalifa University, Abu Dhabi, UAE

Funder

Natural Sciences and Engineering Research Council of Canada

Polytechnique Montréal

Lebanese American University

Khalifa University of Science, Technology and Research

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Information Systems and Management,Computer Networks and Communications,Computer Science Applications,Hardware and Architecture

Link

Reference34 articles.

1. Free-rider attacks on model aggregation in federated learning;fraboni;Proc 24th Int Conf Artif Intell Statist,2021

4. PASS: Parameters audit-based secure and fair federated learning scheme against free rider;wang,2022

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