Precision Guided Approach to Mitigate Data Poisoning Attacks in Federated Learning

arXiv:2404.04139v1 Announce Type: new
Abstract: Federated Learning (FL) is a collaborative learning paradigm enabling participants to collectively train a shared machine learning model while preserving the privacy of their sensitive data. Nevertheless, the inherent decentralized and data-opaque characteristics of FL render its susceptibility to data poisoning attacks. These attacks introduce malformed or malicious inputs during local model training, subsequently influencing the global model and resulting in erroneous predictions. Current FL defense strategies against data poisoning attacks either involve a trade-off …

arxiv attacks cs.ai cs.cr data data poisoning decentralized federated federated learning machine machine learning opaque paradigm poisoning poisoning attacks privacy sensitive sensitive data train