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pro vyhledávání: '"KHAN, Ahmad"'
Personalized Federated Learning (pFL) holds immense promise for tailoring machine learning models to individual users while preserving data privacy. However, achieving optimal performance in pFL often requires a careful balancing act between memory o
Externí odkaz:
http://arxiv.org/abs/2409.06805
Estimating treatment effects from observational data is paramount in healthcare, education, and economics, but current deep disentanglement-based methods to address selection bias are insufficiently handling irrelevant variables. We demonstrate in ex
Externí odkaz:
http://arxiv.org/abs/2407.20003
Autor:
Khan, Ahmad Ali, Adve, Raviraj
Improving throughput for cell-edge users through coordinated resource allocation has been a long-standing driver of research in wireless cellular networks. While a variety of wireless resource management problems focus on sum utility, max-min utility
Externí odkaz:
http://arxiv.org/abs/2403.16344
Autor:
Khan, Ahmad Ali, Adve, Raviraj
Part I of this two-part paper focused on the formulation of percentile problems, complexity analysis, and development of power control algorithms via the quadratic fractional transform (QFT) and logarithmic fractional transform (LFT) for sum-least-qt
Externí odkaz:
http://arxiv.org/abs/2403.16343
Publikováno v:
Emerald Emerging Markets Case Studies, 2024, Vol. 14, Issue 4, pp. 1-35.
Externí odkaz:
http://www.emeraldinsight.com/doi/10.1108/EEMCS-12-2022-0543
Collaborations among various entities, such as companies, research labs, AI agents, and edge devices, have become increasingly crucial for achieving machine learning tasks that cannot be accomplished by a single entity alone. This is likely due to fa
Externí odkaz:
http://arxiv.org/abs/2305.17052
Autor:
Khan, Ahmad Faraz, Wang, Xinran, Le, Qi, Abdeen, Zain ul, Khan, Azal Ahmad, Ali, Haider, Jin, Ming, Ding, Jie, Butt, Ali R., Anwar, Ali
Existing incentive solutions for traditional Federated Learning (FL) focus on individual contributions to a single global objective, neglecting the nuances of clustered personalization with multiple cluster-level models and the non-monetary incentive
Externí odkaz:
http://arxiv.org/abs/2304.07514