QuAFL: Federated Averaging Can Be Both Asynchronous and Communication-Efficient. - Hossein Zakerinia; Shayan Talaei; Giorgi Nadiradze; Dan Alistarh (CoRR, 2022)

PeFLL: A Lifelong Learning Approach to Personalized Federated Learning. - Jonathan Scott; Hossein Zakerinia; Christoph H. Lampert (CoRR, 2023)

Communication-Efficient Federated Learning With Data and Client Heterogeneity. - Hossein Zakerinia; Shayan Talaei; Giorgi Nadiradze; Dan Alistarh (AISTATS, 2024)

PeFLL: Personalized Federated Learning by Learning to Learn. - Jonathan Scott; Hossein Zakerinia; Christoph H. Lampert (ICLR, 2024)

More Flexible PAC-Bayesian Meta-Learning by Learning Learning Algorithms. - Hossein Zakerinia; Amin Behjati; Christoph H. Lampert (ICML, 2024)

DP-KAN: Differentially Private Kolmogorov-Arnold Networks. - Nikita P. Kalinin; Simone Bombari; Hossein Zakerinia; Christoph H. Lampert (CoRR, 2024)

Deep Multi-Task Learning Has Low Amortized Intrinsic Dimensionality. - Hossein Zakerinia; Dorsa Ghobadi; Christoph H. Lampert (CoRR, 2025)

Fast Rate Bounds for Multi-Task and Meta-Learning with Different Sample Sizes. - Hossein Zakerinia; Christoph H. Lampert (CoRR, 2025)

Federated Learning with Unlabeled Clients: Personalization Can Happen in Low Dimensions. - Hossein Zakerinia; Jonathan Scott; Christoph H. Lampert (CoRR, 2025)