Muhammad Habib ur Rehman – författare

This book discusses the trust models for next-generation Blockchain ecosystems. The book provides a comprehensive discussion on various trust factors involving security, anonymization, reputation, governance, economic models, and other relevant determinants. The book covers various topics in breadth and depth. In addition, it sets the foundation to involve the readers in understanding the core theories supplemented with technical and experimental discussion. The book starts by laying out the foundations of trust models in Blockchain ecosystems. The authors then provide a study of existing trust models Blockchain networks. They then provide identification of trust factors and discuss each trust factor. The book concludes with a future outlook of trust-enabling Blockchain ecosystems.

This book dives deep into both industry implementations and cutting-edge research driving the Federated Learning (FL) landscape forward. FL enables decentralized model training, preserves data privacy, and enhances security without relying on centralized datasets. Industry pioneers like NVIDIA have spearheaded the development of general-purpose FL platforms, revolutionizing how companies harness distributed data. Alternately, for medical AI, FL platforms, such as FedBioMed, enable collaborative model development across healthcare institutions to unlock massive value.

Research advances in PETs highlight ongoing efforts to ensure that FL is robust, secure, and scalable. Looking ahead, federated learning could transform public health by enabling global collaboration on disease prevention while safeguarding individual privacy. From recommendation systems to cybersecurity applications, FL is poised to reshape multiple domains, driving a future where collaboration and privacy coexist seamlessly.