Securing Meta-Learning: Methods and Applications

Meta-learning frameworks must be secured due to data sensitivity and adversaries.The three main security methods are homomorphic encryption (HE), differential privacy (DP), and Federated Learning (FL).Each approach is tested for accuracy, attack resistance, computing efficiency, and scalability.HE offers data confidentiality with encrypted computations but substantial processing expense.By injecting noise, DP balances privacy and accuracy.FL improves privacy and scalability through decentralized learning, but communication cost and non-IID data issues remain.Application needs determine method: HE for high secrecy, DP for robust privacy, FL for decentralized applications.HE, DP, and FL hybrid models should be studied to increase computational efficiency and manage non-IID data in secure meta-learning applications in healthcare, banking, and IoT networks.