Privacy Preserving Distributed Image Processing Using Federated Learning and CNNs

The combination of Federated Learning (FL) and Convolutional Neural Networks (CNNs) has surfaced as a groundbreaking method for distributed image processing, tackling significant issues in data privacy and computational efficiency. The decentralized framework of FL addresses privacy concerns by allowing collaborative model training without the need to share raw data, while CNNs improve performance by effectively extracting features from diverse datasets. Despite these advancements, challenges such as significant computational demands, non-IID data, and vulnerability to adversarial attacks hinder the broader adoption of this approach. This review highlights the current state of FLCNN integration, underscoring key accomplishments and identifying persistent research gaps. Future directions involve the development of lightweight architectures, application of model compression and knowledge distillation, and improvement of security through Differential Privacy and Secure Multiparty Computation. Broadening FL to include video data, multimodal learning, and Edge AI offers considerable opportunities for scalable, resource-efficient solutions across various application domains. Overcoming these challenges will allow FL and CNNs to revolutionize critical fields such as healthcare, autonomous systems, and surveillance, promoting ethical, inclusive, and privacy-preserving advancements in distributed machine learning. This review underscores the importance of these efforts in driving impactful, global innovations.