most citedEnhanced Privacy and Communication Efficiency in Non-IID Federated Learning with Adaptive Quantization and Differential Privacy

5 citations · 9 across the 2 of their papers we have counts for

collaborators

2 papers

cs.CV2026★ 4 cited

Sample Selection Using Multi-Task Autoencoders in Federated Learning with Non-IID Data

Emre Ardıç, Yakup Genç

Federated learning is a machine learning paradigm in which multiple devices collaboratively train a model under the supervision of a central server while ensuring data privacy. How…

cs.CV2026★ 5 cited

Enhanced Privacy and Communication Efficiency in Non-IID Federated Learning with Adaptive Quantization and Differential Privacy

Emre Ardıç, Yakup Genç

Federated learning (FL) is a distributed machine learning method where multiple devices collaboratively train a model under the management of a central server without sharing under…