Towards a Robust Federated Learning Architecture with Modular Aggregation Strategies

We present a novel federated learning (FL) framework designed to enhance privacy, security, and efficiency in decentralized machine learning. The framework features a modular architecture that simplifies deployment and customization while supporting flexible aggregation strategies. Robust security mechanisms, including encrypted communication and decentralized model updates, ensure data confidentiality and integrity. Performance evaluation through controlled experiments on image classification and recommendation tasks across multiple edge devices demonstrates its ability to achieve competitive model accuracy while optimizing computational and communication efficiency. These findings underscore the framework's effectiveness in privacy-sensitive, distributed learning environments.