Quantization-based chained privacy-preserving federated learning

Introduction

In the current era of explosive data growth, machine learning plays a vital role in data mining and is widely applied in numerous fields^1,2. However, the traditional centralized machine learning paradigm has significant drawbacks. It requires storing many raw datasets centrally on a single central server, which undoubtedly brings serious risks to user data privacy leakage^3,4. Especially in sensitive areas such as medical diagnosis, the data may contain extremely sensitive personal information that is strictly prohibited from being directly shared with third parties⁵. Moreover, large-scale data transmission imposes a massive burden on network resources, making the application of traditional centralized machine learning in practical scenarios face numerous difficulties.

Federated learning is a promising decentralized learning paradigm to solve these problems and is gradually attracting widespread attention^6,7. In the federated learning framework, multiple geographically dispersed clients can train machine learning models locally using their raw data without transmitting them to a central server, thus effectively decreasing the risk of data privacy leakage. Specifically, the aggregation server distributes the parameters of the global model to selected clients in each training round. These clients then train local models based on their data and upload the models’ parameters to the aggregation server. Subsequently, the server fuses these models’ parameters to compute a globally updated model⁸. Through this approach, federated learning achieves model optimization and knowledge fusion while keeping the data local, gradually improving the global model’s performance. Among numerous federated learning algorithms, Fedavg⁹ is a typical and widely used scheme.

Although federated learning has many advantages, it still faces a series of serious challenges¹⁰. For example, in edge computing environments¹¹, limited bandwidth and computational resources greatly restrict the performance of federated learning. Specifically, on the one hand, the frequent communication between selected clients and the aggregation server leads to high communication costs, which becomes a key factor restricting system performance in bandwidth-constrained edge computing scenarios^{12, 13, 14, 15–16}. On the other hand, model updating and parameter transfer involve a large quantity of data transfer and computation, which not only increases the computational complexity but also may reduce the convergence of model training. In addition, security issues are also an important aspect...