Federated Learning at Scale: A Privacy-Preserving Framework for Decentralized AI Training

In the era of big data, how to balance model training with privacy of users’ data presents as a difficult problem. We present Federated Learning at Scale: A Systematization of Existing Work and a Blueprint for Future Research, which enables large-scale collaborative deep learning across many clients without centralizing data. Unlike standard machine learning methods, which aggregate raw data in one location, our federated approach ensures that client data resides on the corresponding devices. Through FedAvg, rather than sharing sensitive data, model updates are shared and aggregated by a central server. The proposed architecture involves necessary privacy aware techniques like isolated local training, secure communication protocol, and compatibility with Differential Privacy (DP) and Secure Aggregation (SA) algorithms. We evaluate the scalability and effectiveness of our approach on the MNIST dataset, with 5 simulated clients. Results from ten rounds of communication demonstrate the effectiveness of the DEGRA model and show that accuracy, precision, recall and F1-score improve significantly (from 66.43% to 96.42%), and the model can achieve a performance comparable to that of centralized training in a decentralized and privacy-conscious manner. This model is designed for edge scalability, resilient to data heterogeneity and low communication overhead, making it especially suitable for deployment in real applications, for example, mobile health, banking and IoT systems. The architecture itself is lightweight and flexible for easy scaling-up to more data and more complex non-IID data distributions. The proposed federated learning model can effectively tradeoff between performance and privacy and can support the construction of secure, scalable, and trustworthy AI systems. The code is available on the GitHub repository https://github.com/IndrasenaManga/FederatedLearning-at-Scale-A-Privacy-Preserving-Framework-for-Decentralized-AI-Training