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pro vyhledávání: '"KHAN, AHMAD FARAZ"'
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
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
Autor:
Khan, Ahmad Faraz, Li, Yuze, Wang, Xinran, Haroon, Sabaat, Ali, Haider, Cheng, Yue, Butt, Ali R., Anwar, Ali
Federated Learning (FL) is a machine learning approach that addresses privacy and data transfer costs by computing data at the source. It's particularly popular for Edge and IoT applications where the aggregator server of FL is in resource-capped edg
Externí odkaz:
http://arxiv.org/abs/2204.07767
Publikováno v:
Academy of Management Annual Meeting Proceedings. 2023, Vol. 2023 Issue 1, p1-6. 6p.
Publikováno v:
Benchmarking: An International Journal, 2017, Vol. 24, Issue 3, pp. 570-593.
Externí odkaz:
http://www.emeraldinsight.com/doi/10.1108/BIJ-05-2016-0078
Autor:
Khan, Ahmad Faraz, Talib, Parvaiz
Publikováno v:
Indian Journal of Industrial Relations, 2016 Oct 01. 52(2), 307-320.
Externí odkaz:
https://www.jstor.org/stable/44840816
Autor:
Khan, Ahmad Faraz, Wang, Xinran, Le, Qi, Khan, Azal Ahmad, Ali, Haider, Ding, Jie, Butt, Ali, Anwar, Ali
Personalized FL has been widely used to cater to heterogeneity challenges with non-IID data. A primary obstacle is considering the personalization process from the client's perspective to preserve their autonomy. Allowing the clients to participate i
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::489556109caa8883dad4cbd113e4e530
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