Techniques for Secure Data Aggregation in Federated Learning

Several techniques are employed to achieve secure data aggregation in federated learning, each with its own strengths and challenges. Cryptographic protocols form the backbone of secure aggregation, ensuring that individual data contributions are masked and remain confidential. Homomorphic encryption allows computations to be performed on encrypted data without needing decryption, preserving privacy while enabling aggregation. Secure multi-party computation (SMPC) is another technique where multiple parties jointly compute a function over their inputs while keeping those inputs private. Differential privacy adds noise to the data before aggregation, ensuring that individual data points cannot be identified. While these techniques provide robust solutions for secure data aggregation, they also come with challenges, such as increased computational overhead and complexity in implementation. Understanding and overcoming these challenges are crucial for successfully implementing secure aggregation at scale. This section explored various techniques for secure data aggregation and the associated challenges.