# Micro-FL: A Fault-Tolerant Scalable Microservice-Based Platform for Federated Learning
## 1 Introduction
While these platforms often emphasize user scalability and fault management at the user-end, they frequently neglect the vital aspect of server-side fault management and system scalability as the user base expands. Ideally, a federated learning system should inherently possess scalability and fault tolerance. Scalability pertains to the capacity of the system to include additional devices in the federated learning process. More devices can improve the accuracy and speed up the convergence of the federated learning process [18; 19]. Techniques such as resource optimization, prioritizing devices with high computational power, and implementing compression schemes for learning model parameter transfer, can aid in scalability. Effective resource optimization allows more devices to participate in the federated learning process, thereby enhancing performance. However, increasing device participation requires an expansion of server-side computational resources. Fault-tolerance in the context of federated learning indicates the system's capability to manage the federated learning process effectively, even when the server fails. Traditional federated learning, which relies on a centralized cloud server for global aggregation, can be disrupted if the aggregation server malfunctions [20]. Current concerns about fault tolerance in federated learning systems often revolve around adversarial or Byzantine attacks targeting the central server [21; 22; 23; 24] and data related faults such as handling missing data [25]. While numerous studies have investigated the system performance, research into the effects of server faults, such as physical damage, on the federated learning system's performance is relatively scant. To achieve these properties for a performant federated learning system, this paper presents Micro-FL: a microservice-based federated learning platform designed to handle an expanding user base, guarantee high availability, and offer fault tolerance. Suitable for deployment on-site or in the cloud, it leverages the flexibility, modularity, scalability, and reliability that microservices provide. Micro-FL streamlines the testing, deployment, and maintenance of federated learning algorithms, while allowing dynamic resource allocation based on workload or user count, thus improving resource efficiency. The principal contributions of this research paper are as follows:  
1. Introduction of a Microservice-based Federated Learning (Micro-FL) Platform: This research paper proposes a novel Micro-FL platform that leverages microservices architecture to address the challenges of fault tolerance and scalability of federated learning systems. This approach significantly differs from the traditional centralized federated learning frameworks by decomposing the monolithic architecture of the traditional centralized federated learning frameworks into smaller, manageable microservices. This decomposition enhances scalability, fortifies fault tolerance, and facilitates efficient resource management for the federated learning server. 2. Emphasis on Server-side Fault Management: Unlike other federated learning frameworks that mainly concentrate on user-end scalability and fault management, the current study emphasizes server-side challenges. This paper presents solutions for managing faults in the communication system of the federated learning system, which is crucial for maintaining the integrity of the federated learning process. 3. Scalability and Dynamic Resource Allocation: The Micro-FL platform facilitates dynamic resource allocation, which allows for the more efficient management of computational resources. This feature is crucial for accommodating a growing number of federated learning clients without compromising performance or reliability. These contributions underscore the innovation and significance of the proposed Micro-FL platform in enhancing the resilience, efficiency, and scalability of federated learning systems.