# Micro-FL: A Fault-Tolerant Scalable Microservice-Based Platform for Federated Learning
## 3 Related Work
The Federated Learning and Differential Privacy (FL&DP) [44] Framework is an open-source framework that uses TensorFlow for deep learning tasks and the SciKit-Learn library for linear models and clustering. It offers various aggregation algorithms and uses adaptive Differential Privacy and randomized response coins to enhance data privacy protection during the learning process. LEAF [45] is an open-source benchmark tailored for federated learning settings. It provides open-source datasets suitable for federated learning, metrics for evaluating federated learning algorithms, and a repository of standard methods such as minibatch Stochastic Gradient Descent and Federated Averaging. Serving as a valuable resource, LEAF aids in benchmarking and comparing federated learning algorithms. Flower [46] is an open-source framework that supports large-cohort training on edge devices and compute clusters. It offers aggregation methods like SecAgg [47] and SecAgg+ [48], and supports both simulation and federated modes of operation. Notably, Flower is language and machine learning framework-agnostic, ensuring broad compatibility. Serverless federated learning (FedLess) [49] is a system designed for federated learning on diverse Function-as-a-Service platforms, supporting major commercial FaaS platforms such as AWS Lambda, Google Cloud Functions, Azure Functions, and IBM Cloud Functions. Implemented in Python3, FedLess provides a command-line tool for orchestrating the training process and supports TensorFlow and Keras for deep learning models. Its default federated learning strategy is the FedAvg algorithm, commonly used for the aggregation of model updates. Other research projects, such as [50], have also adopted a serverless design for federated learning. FedML [51] is an open-source research library and benchmark platform designed to aid the development of Federated Learning algorithms and provide objective performance comparisons. It supports on-device training, distributed computing, and single machine simulation. FedML offers resources such as algorithmic implementations, benchmarks with evaluation metrics, access to real-world datasets, and validated baseline results. Itis organized into FedML-API for high-level APIs and FedML-core for low-level APIs, using the Message Passing Interface for system communication. FedML supports various federated learning algorithms including FedAvg, Decentralized FL, Vertical Federated Learning, and Split Learning. Numerous other tools are designed to address scalability issues in federated learning systems, mainly with regard to scaling in the number of clients [52; 53; 54; 55; 56]. Despite providing several beneficial features, these platforms fail to implement an efficient central server design that is scalable and fault tolerant. Micro-FL is introduced to mitigate these deficiencies. Based on the principles of microservices system architecture, Micro-FL is designed to enhance the scalability and robustness of centralized federated learning systems, effectively addressing these significant gaps in contemporary solutions.