Privacy For Free: Wireless Federated Learning Via Uncoded Transmission With Adaptive Power Control (2006.05459v4)

Abstract: Federated Learning (FL) refers to distributed protocols that avoid direct raw data exchange among the participating devices while training for a common learning task. This way, FL can potentially reduce the information on the local data sets that is leaked via communications. In order to provide formal privacy guarantees, however, it is generally necessary to put in place additional masking mechanisms. When FL is implemented in wireless systems via uncoded transmission, the channel noise can directly act as a privacy-inducing mechanism. This paper demonstrates that, as long as the privacy constraint level, measured via differential privacy (DP), is below a threshold that decreases with the signal-to-noise ratio (SNR), uncoded transmission achieves privacy "for free", i.e., without affecting the learning performance. More generally, this work studies adaptive power allocation (PA) for decentralized gradient descent in wireless FL with the aim of minimizing the learning optimality gap under privacy and power constraints. Both orthogonal multiple access (OMA) and non-orthogonal multiple access (NOMA) transmission with "over-the-air-computing" are studied, and solutions are obtained in closed form for an offline optimization setting. Furthermore, heuristic online methods are proposed that leverage iterative one-step-ahead optimization. The importance of dynamic PA and the potential benefits of NOMA versus OMA are demonstrated through extensive simulations.

• The paper introduces a method to harness inherent channel noise for differential privacy, ensuring performance is maintained when SNR conditions are met.
• It presents an adaptive power control strategy that dynamically optimizes learning performance while satisfying privacy and power constraints.
• The study shows that using NOMA with adaptive power control outperforms OMA, improving convergence rates and bandwidth efficiency.

Privacy For Free: Wireless Federated Learning Via Uncoded Transmission With Adaptive Power Control

The paper by Dongzhu Liu and Osvaldo Simeone addresses the integration of federated learning (FL) with wireless communication systems, focusing on the challenges of privacy preservation when using uncoded transmission and adaptive power control. The authors propose techniques for ensuring differential privacy (DP) in federated learning systems without incurring a performance penalty, which they refer to as obtaining privacy "for free."

Key Highlights and Numerical Results

The authors explore both orthogonal multiple access (OMA) and non-orthogonal multiple access (NOMA) techniques for FL, particularly focusing on over-the-air learning to maximize bandwidth efficiency. The paper elucidates conditions under which privacy constraints do not degrade learning performance – specifically, when the differential privacy constraint level is below a threshold that depends on the signal-to-noise ratio (SNR).

1. Differential Privacy through Channel Noise: By leveraging the inherent channel noise in wireless communication, the paper shows that privacy guarantees, measured in terms of differential privacy, can be ensured without requiring additional noise-inducing mechanisms, provided the SNR is sufficiently low.
2. Adaptive Power Control: The authors detail an optimal adaptive power allocation strategy that balances the learning performance against privacy and power constraints. Through obtaining analytical expressions, they reveal that dynamic power allocations outperform static allocations, which do not adapt based on iteration-specific requirements.
3. Comparison between OMA and NOMA: Extensive simulations demonstrate the advantages of using NOMA over traditional OMA in terms of efficiency and privacy when appropriate power control is applied. Notably, adaptive power control schemes were seen to offer significant improvements in convergence rates and learning results, highlighting the sub-optimality of transmitting with full power or additional noise under certain DP constraints.
4. Privacy "for Free": The concept of achieving privacy without performance loss is shown to be viable under specific SNR conditions. The authors found that conditions related to dataset sizes and the choice of learning parameters impact this threshold, offering insight into when privacy can be truly "free."

Practical and Theoretical Implications

The work has several significant implications. Practically, the paper facilitates the implementation of FL in communication-constrained environments such as edge computing, where bandwidth and privacy are critical. From a theoretical standpoint, it underscores the importance of integrating communication-theoretic insights into federated learning design, particularly in adaptive systems that can benefit from non-ideal communication channels.

Speculation on future developments points to the integration of these methods into more complex network topologies and real-world datasets. Further research could explore the interplay of digital communication strategies with privacy-preserving mechanisms to achieve more robust and efficient federated learning solutions on a broader scale.

This paper provides a strong foundation for advancing federated learning over wireless systems, promoting a holistic optimization approach that concurrently addresses learning performance and privacy, paving the way for more resilient deployment in practical applications.