The field of decentralized systems and federated learning is moving towards more efficient and secure solutions. Researchers are exploring new protocols and architectures to improve the performance and scalability of decentralized networks. One of the key areas of focus is on developing more effective NAT traversal techniques, which are crucial for enabling decentralized peer-to-peer communication. Additionally, federated learning is being investigated as a means of training machine learning models in a decentralized and privacy-preserving manner. This approach has shown promising results in various applications, including thermal urban feature segmentation and ransomware detection. Notable papers in this area include:

• A large-scale measurement study of a fully decentralized NAT traversal protocol, which provides new insights into the performance of such protocols and challenges existing assumptions.
• A comparison of centralized and decentralized approaches to federated learning for thermal urban feature segmentation, which highlights the potential benefits of decentralized learning in real-world scenarios.
• A proposal for a new framework for high-performance distributed trust, which enables secure collaborative data analytics and AI by leveraging fast datacenter-type LANs.
• An evaluation of federated learning for privacy-preserving ransomware detection, which demonstrates the effectiveness of this approach in improving detection accuracy while preserving data privacy.