Wireless Federated Learning with Local Differential Privacy (2002.05151v1)

Abstract: In this paper, we study the problem of federated learning (FL) over a wireless channel, modeled by a Gaussian multiple access channel (MAC), subject to local differential privacy (LDP) constraints. We show that the superposition nature of the wireless channel provides a dual benefit of bandwidth efficient gradient aggregation, in conjunction with strong LDP guarantees for the users. We propose a private wireless gradient aggregation scheme, which shows that when aggregating gradients from $K$ users, the privacy leakage per user scales as $\mathcal{O}\big(\frac{1}{\sqrt{K}} \big)$ compared to orthogonal transmission in which the privacy leakage scales as a constant. We also present analysis for the convergence rate of the proposed private FL aggregation algorithm and study the tradeoffs between wireless resources, convergence, and privacy.

• The paper proposes a novel wireless gradient aggregation scheme that leverages the physical superposition property of wireless channels to enhance privacy in federated learning.
• It demonstrates that this wireless scheme significantly reduces privacy leakage per user as the number of users increases, unlike orthogonal transmission methods.
• The work provides theoretical analysis and simulation results showing the convergence rate benefits and exploring the critical trade-offs between privacy noise and model convergence in wireless FL systems.

Wireless Federated Learning with Local Differential Privacy

The paper "Wireless Federated Learning with Local Differential Privacy" tackles the intersection of federated learning (FL) over wireless channels and privacy constraints, specifically focusing on local differential privacy (LDP). The authors consider the deployment of ML models in scenarios where multiple users participate in the training process over a wireless communication network modeled as a Gaussian multiple access channel (MAC). A key question addressed in this paper is whether the superposition property of the wireless channel can enhance user privacy during federated learning processes, and how this affects the trade-offs among wireless resources, convergence rates, and privacy guarantees.

Main Contributions

The authors propose a novel wireless gradient aggregation scheme that leverages the physical properties of wireless channels to reduce privacy leakages mathematically. The key highlights of this scheme are as follows:

1. Enhanced Privacy through Channel Superposition: The paper demonstrates that the privacy leakage per user decays as $\mathcal{O}(1/\sqrt{K})$, where $K$ is the number of users. This provides a distinct advantage over orthogonal transmission, where privacy leakage remains constant regardless of the number of users.
2. Convergence Analysis: The paper provides a comprehensive analysis of the convergence rate of the proposed FL algorithm considering LDP constraints. It shows that as the number of users increases, the convergence rate approaches that of a centralized solution with the full dataset available to one server.
3. Privacy-Utility Trade-offs: The paper explores the trade-offs between the noise added for ensuring privacy and its impact on the convergence rates. It provides an optimized solution for choosing noise parameters to maximize convergence while satisfying specific privacy levels for each user.
4. Implementation and Simulation: The proposed methods have been evaluated through simulations on a synthetic linear regression task, providing empirical backing to the theoretical claims. The results demonstrate the efficacy of the proposed scheme under varying numbers of users, iterations, and transmit powers.

Theoretical and Practical Implications

This work has significant theoretical implications by extending the boundaries of FL in wireless communications with privacy constraints. The proposed use of the superposition property of Gaussian MAC to provide natural noise aggregation is not only resource-efficient but also presents a new perspective on using the inherent features of a communication medium to enhance privacy.

From a practical standpoint, this research offers a pathway to implement privacy-preserving ML models in edge computing scenarios, particularly in bandwidth-limited and privacy-sensitive environments. As ML models become more prevalent in applications involving personal data (e.g., healthcare, finance), ensuring stringent privacy without compromising on efficiency or accuracy is crucial. The approach presented in this paper could significantly shape how future systems are architected to balance these requirements.

Speculation on Future Developments

The introduction of this wireless gradient aggregation method opens several promising pathways for future research. Extending this work to consider more complex channel conditions such as fading, interference, and varying signal-to-noise ratios could offer deeper insights. Moreover, exploring multiple-antennas configurations or considering video data streams could provide additional practical benefits. There is also potential for this research to inform developments in software and hardware implementations that could make FL with LDP feasible and efficient in consumer-grade devices.

In conclusion, this paper methodically explores the fusion of federated learning and wireless communications under privacy constraints, offering both theoretical insights and practical solutions. It stands as a relevant contribution as the field progresses toward deploying distributed AI solutions in real-world settings while maintaining robust privacy standards.