Exploring the Performance Recovery of Remedial Learning Within the Federated Learning Framework

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.