#federated-learning

#federated-learning

AI and ML are critical for enabling autonomous, self-optimizing Wi-Fi networks capable of managing dense deployments and real-time performance demands. AI/ML reduces operational costs, improves reliability and security and delivers a more consistent quality of experience. Proprietary approaches, inconsistent data quality, and closed interfaces slow innovation and increase integration costs. Interoperable frameworks - not algorithms - will be key to success. Interoperability must include data models, telemetry, APIs, and model lifecycle management.

The 5th International Conference on Computing and Communication Networks (ICCCNet-2025) concluded on a high note at Manchester Metropolitan University, solidifying its reputation as a premier platform for global innovation. From August 1-3, 2025, the conference became a crucible for ideas, bringing together brilliant minds from academia, industry, and government to forge the future of technology. The prestigious best paper awards, announced at the close of the event, weren't just accolades; they were a roadmap to a more intelligent, sustainable, and equitable world.

Federated learning is a distributed (decentralized) ML technique that enables training models by moving the training process to where the data is, instead of collecting and moving the data to the central server.