Robust Federated Learning Against Data Poisoning: A Split Learning-Based Approach Evaluated on Various Aggregation Techniques

With the growing importance of privacy in data-driven applications, ensuring the security and confidentiality of personal information has become a significant challenge. Federated Learning (FL) offers a promising solution by enabling collaborative model training across multiple clients while keeping individual data localized and private. In FL, outputs computed by various devices are aggregated at a central server, which uses iterative algorithms to develop a globally shared model. However, the presence of malicious participants can result in the intentional manipulation of training data or the model, compromising the system's accuracy and reliability. In this study, we propose a novel FL technique based on Split Learning (SL) to enhance robustness against data poisoning attacks. Our approach aims to develop a robust FL system based on SL, integrate it with existing aggregation methods, and compare its performance with traditional FL approaches.