Federated Learning with Differentially Private Loss Approximations

Core Concepts

FedLAP-DP proposes a novel approach to federated learning that approximates local loss landscapes using synthetic samples, enabling unbiased global optimization on the server side. This method outperforms traditional gradient-sharing schemes, especially under tight privacy budgets and highly skewed data distributions.

Abstract

The paper introduces FedLAP-DP, a novel differentially private framework for federated learning that addresses the limitations of existing gradient-sharing approaches.

Key highlights:

1. Conventional federated learning methods like FedAvg rely on aggregating local model updates, which can lead to performance degradation under data heterogeneity and differential privacy (DP) mechanisms. This is due to the inconsistency between local and global objectives.
2. FedLAP-DP proposes a new approach that transmits synthetic samples approximating the local loss landscapes, enabling the server to faithfully uncover the global loss landscape and perform unbiased global optimization.
3. The synthetic samples are optimized to match the gradients computed on the real client data, while respecting a trusted region around the initial model. This mitigates the bias introduced by imperfect local approximations.
4. FedLAP-DP integrates record-level differential privacy by applying DP-SGD to the gradients computed on real client data. This provides theoretical privacy guarantees without incurring additional privacy costs.
5. Extensive experiments demonstrate the superiority of FedLAP-DP over existing gradient-sharing baselines in terms of performance and convergence speed, especially under tight privacy budgets and highly skewed data distributions.
6. FedLAP-DP also offers communication efficiency benefits, as transmitting synthetic samples can be less costly than transferring gradients in each round. Additionally, the method can further improve performance by synthesizing a larger set of samples, enabling a trade-off between communication costs and utility.

Stats

FedLAP-DP achieves superior performance compared to gradient-sharing baselines under tight privacy budgets and highly skewed data distributions.
FedLAP-DP exhibits faster convergence speed compared to typical gradient-sharing methods.

Quotes

"FedLAP-DP consumes the same privacy costs as traditional gradient-sharing baselines."
"FedLAP-DP provides reliable utility under privacy-preserving settings, especially when considering low privacy budgets and highly skewed data distributions."

Key Insights Distilled From

FedLAP-DP: Federated Learning by Sharing Differentially Private Loss Approximations

by Hui-Po Wang,... at arxiv.org 05-06-2024

https://arxiv.org/pdf/2302.01068.pdf

Deeper Inquiries

How can FedLAP-DP be extended to handle scenarios where clients want to keep their label class information confidential from others

To handle scenarios where clients want to keep their label class information confidential from others, FedLAP-DP can implement a privacy-preserving technique known as secure multi-party computation (MPC). In this approach, each client encrypts their label class information before sharing it with the server. The server performs computations on the encrypted data without decrypting it, ensuring that the label class information remains confidential. By using MPC, FedLAP-DP can maintain the privacy of sensitive information while still allowing for collaborative model training.

What are the potential limitations of FedLAP-DP in terms of scalability and its applicability to larger models or more complex datasets

One potential limitation of FedLAP-DP in terms of scalability is the communication overhead involved in transmitting synthetic samples between clients and the server. As the size of the synthetic samples increases or the number of clients grows, the communication costs may become prohibitive, especially for larger models or more complex datasets. Additionally, the computational resources required to generate and process synthetic samples for a large number of clients can pose scalability challenges. Ensuring efficient communication and computation strategies will be crucial for the scalability of FedLAP-DP.

Could the synthetic sample generation process in FedLAP-DP be further improved to better capture the underlying data distribution and enhance the quality of the global optimization

The synthetic sample generation process in FedLAP-DP can be further improved to better capture the underlying data distribution and enhance the quality of global optimization. One way to enhance this process is to incorporate advanced generative modeling techniques, such as variational autoencoders or generative adversarial networks, to generate more realistic and diverse synthetic samples. By training the generative models on a representative subset of the client data, FedLAP-DP can create synthetic samples that closely mimic the data distribution, leading to more accurate approximations of the local loss landscapes and improved global optimization results. Additionally, exploring techniques like data augmentation and transfer learning can help in generating more diverse and informative synthetic samples for better model training.