Privacy and Accuracy Implications of Model Complexity and Integration in Heterogeneous Federated Learning.

Gergely Dániel Németh

Gergely Dániel Németh Miguel Angel Lozano Novi Quadrianto Nuria Oliver Privacy and Accuracy Implications of Model Complexity and Integration in Heterogeneous Federated Learning. 40258-40274 2025 13 IEEE Access https://doi.org/10.1109/ACCESS.2025.3546478 db/journals/access/access13.html#NemethLQO25 streams/journals/access

Gergely Dániel Németh Eros Fanì Yeat Jeng Ng Barbara Caputo Miguel Angel Lozano Nuria Oliver Novi Quadrianto Diversity-Driven Learning: Tackling Spurious Correlations and Data Heterogeneity in Federated Models. 2025 April abs/2504.11216 CoRR https://doi.org/10.48550/arXiv.2504.11216 db/journals/corr/corr2504.html#abs-2504-11216 streams/journals/corr

Gergely Dániel Németh Miguel Angel Lozano Novi Quadrianto Nuria Oliver Addressing Membership Inference Attack in Federated Learning with Model Compression. 2023 abs/2311.17750 CoRR https://doi.org/10.48550/arXiv.2311.17750 db/journals/corr/corr2311.html#abs-2311-17750

Gergely Dániel Németh Miguel Angel Lozano Novi Quadrianto Nuria Oliver Ramirez A Snapshot of the Frontiers of Client Selection in Federated Learning. 2022 2022 Trans. Mach. Learn. Res. https://openreview.net/forum?id=vwOKBldzFu db/journals/tmlr/tmlr2022.html#NemethLQR22

Gergely Dániel Németh Miguel Angel Lozano Novi Quadrianto Nuria Oliver A Snapshot of the Frontiers of Client Selection in Federated Learning. 2022 abs/2210.04607 CoRR https://doi.org/10.48550/arXiv.2210.04607 db/journals/corr/corr2210.html#abs-2210-04607

Barbara Caputo Eros Fanì Miguel Angel Lozano Yeat Jeng Ng Nuria OliverNuria Oliver Ramirez Novi Quadrianto