Report on Recent Developments in Federated Learning and Differential Privacy
General Direction of the Field
The recent advancements in federated learning (FL) and differential privacy (DP) have been focused on enhancing the privacy-utility trade-offs while addressing communication inefficiencies and security vulnerabilities. The field is moving towards more sophisticated privacy mechanisms that not only protect data but also improve the overall efficiency of the learning process. This is being achieved through the integration of cryptographic techniques, novel privacy amplification methods, and adaptive strategies for gradient clipping and noise addition.
One of the key trends is the adoption of the shuffle model of differential privacy in FL. This model leverages intermediate shuffling operations to achieve privacy amplification, thereby reducing the noise required for privacy guarantees. This approach is seen as a promising direction to balance privacy and utility, especially in scenarios where communication efficiency is critical.
Another significant development is the exploration of client-side privacy risks in FL. Recent studies have highlighted the potential for data reconstruction attacks by honest-but-curious clients, emphasizing the need for more robust privacy-preserving mechanisms that protect against both server-side and client-side adversaries.
Communication efficiency remains a central concern, with researchers proposing methods that compress gradient updates using differentially private sketches. These methods aim to reduce the communication overhead while ensuring that privacy is maintained through the addition of noise. Adaptive clipping strategies are also being developed to mitigate the bias introduced by traditional clipping methods, thereby improving the overall performance of FL systems.
Noteworthy Papers
Camel: Communication-Efficient and Maliciously Secure Federated Learning in the Shuffle Model of Differential Privacy: Introduces a novel framework that supports integrity checks for shuffle computation, achieving security against malicious adversaries while optimizing communication efficiency.
Federated Learning Nodes Can Reconstruct Peers' Image Data: Demonstrates the risk of client-side data reconstruction attacks, highlighting the need for more robust privacy-preserving mechanisms in FL.
Private and Communication-Efficient Federated Learning based on Differentially Private Sketches: Proposes a method that compresses gradients using differentially private sketches, enhancing communication efficiency and privacy while employing adaptive clipping to improve model accuracy.
