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政大機構典藏 > 資訊學院 > 資訊科學系 > 會議論文 > Item 140.119/159777

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題名:	Exploring the Performance Recovery of Remedial Learning Within the Federated Learning Framework
作者:	廖文宏 Liao, Wen-Hung;Huang, Che-Wei
貢獻者:	資訊系
日期:	2025-08
上傳時間:	2025-10-03 09:53:35 (UTC+8)
摘要:	This research examines the role of remedial learning in federated image classification, focusing on performance recovery when client data is subject to poisoning attack. Remedial learning targets model weaknesses through corrective strategies, aiming to enhance accuracy and stability across heterogeneous data sources. Experimental results show that applying remedial learning collaboratively across all clients in the federated framework yields significantly better performance recovery than isolating the contaminated client. Further evaluation reveals that excluding the contaminated client and retraining the model still surpasses remedial learning applied solely on that client. As training rounds increase, performance converges closely to that of standard federated training. These findings highlight the effectiveness of distributed remedial learning in mitigating the impact of data contamination and improving federated model robustness.
關聯:	Proceedings of the 21st IEEE International Conference on Advanced Visual and Signal-Based Systems, IEEE Signal Processing Society
資料類型:	conference
DOI 連結:	https://doi.org/10.1109/AVSS65446.2025.11149793
DOI:	10.1109/AVSS65446.2025.11149793
顯示於類別:	[資訊科學系] 會議論文

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