Partially Personalized Federated Learning: Breaking the Curse of Data Heterogeneity

29 May 2023

Papers citing "Partially Personalized Federated Learning: Breaking the Curse of Data Heterogeneity"

8 / 8 papers shown

Title
Trial and Trust: Addressing Byzantine Attacks with Comprehensive Defense Strategy Gleb Molodtsov Daniil Medyakov Sergey Skorik Nikolas Khachaturov Shahane Tigranyan Vladimir Aletov A. Avetisyan Martin Takáč Aleksandr Beznosikov AAML 35 0 0 12 May 2025
Personalized Federated Training of Diffusion Models with Privacy Guarantees Kumar Kshitij Patel Weitong Zhang Lingxiao Wang MedIm 50 0 0 01 Apr 2025
Collaborative and Efficient Personalization with Mixtures of Adaptors Abdulla Jasem Almansoori Samuel Horváth Martin Takáč FedML 44 2 0 04 Oct 2024
Fine-Tuning Personalization in Federated Learning to Mitigate Adversarial Clients Youssef Allouah Abdellah El Mrini R. Guerraoui Nirupam Gupta Rafael Pinot FedML 32 0 0 30 Sep 2024
Adaptive Model Pruning and Personalization for Federated Learning over Wireless Networks Xiaonan Liu T. Ratnarajah M. Sellathurai Yonina C. Eldar 32 4 0 04 Sep 2023
FedBN: Federated Learning on Non-IID Features via Local Batch Normalization Xiaoxiao Li Meirui Jiang Xiaofei Zhang Michael Kamp Qi Dou OOD FedML 168 787 0 15 Feb 2021
Privacy and Robustness in Federated Learning: Attacks and Defenses Lingjuan Lyu Han Yu Xingjun Ma Chen Chen Lichao Sun Jun Zhao Qiang Yang Philip S. Yu FedML 183 355 0 07 Dec 2020
Rapid Learning or Feature Reuse? Towards Understanding the Effectiveness of MAML Aniruddh Raghu M. Raghu Samy Bengio Oriol Vinyals 183 639 0 19 Sep 2019