Federated Deep

Federated deep learning (FL) addresses the challenge of training deep learning models on decentralized data, preserving data privacy while enabling collaborative model building across multiple clients. Current research focuses on improving model accuracy and efficiency in heterogeneous data settings, exploring techniques like adaptive obfuscation, knowledge distillation, and novel optimization algorithms tailored for federated architectures (e.g., proximal gradient descent, ADMM). This approach holds significant promise for various applications, including medical image analysis, IoT security, and personalized recommendations, by enabling the development of powerful models from sensitive data without compromising privacy.