Federated Learning with Differential Privacy: Algorithms and Performance Analysis (1911.00222v2)

Abstract: In this paper, to effectively prevent information leakage, we propose a novel framework based on the concept of differential privacy (DP), in which artificial noises are added to the parameters at the clients side before aggregating, namely, noising before model aggregation FL (NbAFL). First, we prove that the NbAFL can satisfy DP under distinct protection levels by properly adapting different variances of artificial noises. Then we develop a theoretical convergence bound of the loss function of the trained FL model in the NbAFL. Specifically, the theoretical bound reveals the following three key properties: 1) There is a tradeoff between the convergence performance and privacy protection levels, i.e., a better convergence performance leads to a lower protection level; 2) Given a fixed privacy protection level, increasing the number $N$ of overall clients participating in FL can improve the convergence performance; 3) There is an optimal number of maximum aggregation times (communication rounds) in terms of convergence performance for a given protection level. Furthermore, we propose a $K$-random scheduling strategy, where $K$ ($1<K<N$) clients are randomly selected from the $N$ overall clients to participate in each aggregation. We also develop the corresponding convergence bound of the loss function in this case and the $K$-random scheduling strategy can also retain the above three properties. Moreover, we find that there is an optimal $K$ that achieves the best convergence performance at a fixed privacy level. Evaluations demonstrate that our theoretical results are consistent with simulations, thereby facilitating the designs on various privacy-preserving FL algorithms with different tradeoff requirements on convergence performance and privacy levels.

• The paper introduces the NbAFL framework that adds noise to client updates before aggregation to ensure (ε, δ)-differential privacy.
• It derives theoretical convergence bounds that reveal a tradeoff between privacy levels and model performance, affected by client participation and communication rounds.
• The study validates its findings through extensive simulations and proposes a K-random scheduling strategy to optimize convergence under privacy constraints.

Federated Learning with Differential Privacy: Algorithms and Performance Analysis

The paper "Federated Learning with Differential Privacy: Algorithms and Performance Analysis" by Kang Wei et al., presents a thorough exploration of federated learning (FL) systems integrated with differential privacy (DP) mechanisms. The primary focus of the work is to introduce a novel framework, termed noising before model aggregation FL (NbAFL), and rigorously analyze its convergence properties under different privacy constraints.

Summary of Contributions

Federated Learning allows distributed devices (clients) to collaboratively train a machine learning model without sharing their private data. However, even though data remains local, exchanging model parameters can potentially reveal private information. To mitigate this, the authors propose employing DP, which adds artificial noise to the parameters before they are aggregated on the server. The paper's contributions can be summarized as follows:

1. Framework Proposal:
   • The NbAFL framework is introduced, which adds noise to local model parameters at the client side before they are sent to the central server. This technique ensures that client data remains private while still contributing to the global model training.
2. Differential Privacy Guarantee:
   • The authors prove that NbAFL satisfies $(\epsilon, \delta)$-DP. They provide a detailed analysis showing that by carefully tuning the variance of the added Gaussian noise, the system can achieve desired privacy levels.
3. Theoretical Convergence Bounds:
   • A theoretical convergence bound for the loss function in the NbAFL framework is derived. The analysis unveils three critical properties:
     1. There is a tradeoff between privacy level and convergence performance.
     2. Increasing the number of participating clients $N$ can enhance convergence performance for a fixed privacy level.
     3. There exists an optimal number of maximum aggregation times (communication rounds) that balance privacy and performance.
4. $K$-Random Scheduling Strategy:
   • The paper introduces a strategy where $K$ clients are randomly selected in each round. They derive the corresponding convergence bound and show that this strategy retains the favorable properties of NbAFL. Additionally, there is an optimal $K$ that maximizes convergence performance for a given privacy level.
5. Empirical Verification:
   • The theoretical results are validated through extensive simulations, demonstrating consistency with analytical findings. Especially, the simulations highlight the tradeoff between privacy and convergence as well as the benefits of the $K$-random scheduling strategy.

Implications and Future Directions

Practical Implications:

• The proposed NbAFL framework can be readily implemented in a variety of FL settings, particularly where privacy is of paramount concern, such as medical data analysis, financial data processing, and IoT applications.
• The derived convergence bounds provide actionable insights for system designers to balance performance and privacy by adjusting parameters such as privacy level, number of clients, and communication rounds.

Theoretical Implications:

• This work bridges the gap between privacy measures and performance guarantees in FL, laying a foundation for future research to explore more sophisticated privacy-preserving mechanisms with provable performance metrics.
• The introduction of the $K$-random scheduling strategy opens new avenues for studying client selection methods in FL. Further exploration could focus on dynamic client selection strategies based on real-time analysis of convergence behavior and privacy needs.

Future Developments:

• There is a potential for extending the NbAFL framework to non-convex optimization problems, which are more prevalent in deep learning scenarios.
• Further research could explore hybrid privacy-preserving techniques combining DP with secure multiparty computation (SMC) to enhance robustness against different types of privacy attacks.
• Another avenue for future work is optimizing the noise addition process using advanced statistical techniques to minimize the impact on model accuracy while ensuring strong privacy guarantees.

In summary, the paper posits a significant advancement in the field of privacy-preserving federated learning by introducing a framework that adeptly balances differential privacy and model performance, substantiated by rigorous theoretical analysis and empirical validation. The insights derived from this work hold substantial potential for both practical applications and future theoretical explorations in federated learning and privacy preservation.