# Improved Asynchronous Federated Learning for Data Injection Pollution
## 1 Introduction  
In recent years, the rapid development of distributed machine learning has increased the attention paid to Federated Learning (FL), which is a collaborative learning framework for privacy protection. Unlike traditional centralized learning models, federated learning allows for multiple participants to train the model locally and update the global model parameters without sharing the raw data. However, in practice, synchronous federated learning often faces problems such as differences in computing power, network latency, and device availability, which require all participants to participate in each iteration simultaneously. To meet these challenges, Asynchronous Federated Learning (AFL) has gradually become a research focus. Aynchronous federated learning allows for different clients to update the global model parameters at different frequencies at different times, thus improving the flexibility and efficiency of the system. Compared with synchronization methods, asynchronous methods can better adapt to computational power and network conditions of different devices, whilereducing the impact of clients with a longer waiting time on the overall training schedule. In recent years, the research has mainly focused on the following aspects:  
(1) Optimization of the algorithm design using an adaptive aggregation strategy: We proposed a variety of adaptive aggregation methods, such as FedAvg based on a weighted average [1], and its improved version, to improve the speed and robustness of model convergence by dynamically adjusting the weight of the client's contribution. Asynchronous gradient descent methods and asynchronous optimization algorithms based on SGD (stochastic gradient descent), such as Async-SGD [2] and its variants, have been widely studied. These algorithms significantly improve the training efficiency by parallelizing the training tasks of the client and the parameter-updating process of the server. (2) Privacy protection mechanism: In AFL, communication latency can lead to data from some clients being used multiple times, increasing the risk of privacy leakages. For this problem, the researchers proposed a method based on differential privacy [3] and homomorphism encryption [4] to ensure the privacy of client data. In response to model theft attacks and intermediate results leakage, some work combines federated learning and federated enhanced learning [5] to further improve security in an asynchronous environment. (3) Model heterogeneity treatment: Clients in AFL often have different computational powers and data distribution rates, leading to inconsistencies in model updates. To this end, the researchers proposed [6], a method based on federated meta-learning, to balance the contributions of different clients by introducing a meta-optimizer to the server side. In addition, for the data heterogeneity problem, some work uses task-decomposition-based methods, such as FedMD [7] and Task-Aware FL [8], to decompose global tasks into multiple subtasks and assign them to different clients. (4) Scalability and fault tolerance: To improve the scalability of AFL, researchers proposed the hierarchical federated learning framework [9], which effectively reduces communication latency and reduces bandwidth consumption by adding intermediate server nodes. In terms of fault tolerance, some work explores how to handle client drops or long unresponsive cases, such as the retransmission mechanism based on timeout detection [10] and the redundant backup policy [11]. (5) Practical application and exploration: AFL has been widely used in multiple fields, such as Medical Data Collaboration [12], recommended system [13], and edge computing [14]. The heterogeneity of the devices and the complexity of the network environment in these application scenarios further promote research on and the development of asynchronous methods. In recent years, federated learning has emerged as a revolutionary approach, addressing challenges related to data privacy, security, and distributed data silos across various domains.