The Power of Federated Learning & Internet of things

The Phoenix Global (PHB) blockchain is powering Nex-Gen DApps that are built to fit customer experience. PHB decentralized applications (DApps) promise optimized scalability and flexibility via enterprise sidechains and multi-tiered smart contracts. Top-level data encryption, a two-fold consensus mechanism for improved agility & performance, security, and advanced interactions are other perks.  What are the roles of Federated Learning & the Internet of Things in PHB deployments?  Federated Learning – A Quick Overview Federated Learning, often referred to as Distributed Artificial Intelligence/Machine Learning, is an approach that facilitates collaborative learning from large datasets belonging to different owners without compromising the privacy of each individual’s raw data.  In other words, it utilizes the computing power of several learning sources to enhance the learning efficiency of a model while offering excellent privacy solutions to all data owners. FL is specifically helpful if the required data is not open source or readily available for strategic or legal reasons. In addition, it seeks to tackle impending privacy and data governance issues by adopting a collaborative model training approach without disclosing sensitive data.  Self-driving cars, for instance, require large sets of real-world data to accelerate learning – utilizing a conventional cloud approach could pose safe...