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 safety challenges. FL can assure data security and fast-paced learning.
- Machine learning (ML) techniques have enjoyed mass adoption in Industry 4.0 and advanced health systems to improve process safety, effectiveness, and efficiency. However, data privacy is not guaranteed, but with FL algorithms, sensitive data are kept safe.
Internet of Things (IoT) – A Quick Outlook
Internet of Things (IoT) is rapidly penetrating all sides of life with the snowballing of AI-empowered applications and other intelligent services.
It facilitates the connection of billions of network-enabled devices – “things” – and utilizes massive amounts of centralized data points.
Due to scalability and growing privacy concerns, traditional artificial intelligence techniques may not find real use cases in emerging IoT networks.
Federated Learning & IoT – Phoenix Global in the mix
While the positives of IoT remain undeniable, the scalability, security, and privacy concerns surrounding it (IoT) remain valid. However, Federated Learning (FL) has surfaced as a collaborative and distributive artificial intelligence (AI) approach to solve these challenges.
With several ongoing conversations in the Blockchain space, large volumes of data collected per time, and the emergence of large consumer enterprises and pilots, it is becoming increasingly evident that Federated Learning will accelerate AI capabilities and use cases.
A fast-growing application of FL can be seen in the China-driven phenomenon. In addition, the need for AI-based insights has continued to resurface, especially with the exponential growth in the demand for data-driven artificial intelligence applications.
Phoenix Global – an independent market player, in conjunction with Seneca ESG and APEX Technologies, is set to offer consumers early enterprise adoption by integrating FL with Phoenix Oracle and current AI models and systems like the APEX IQ.
The integration with enterprise-ready Oracle will ensure that Federated Learning applications solve issues around consumer data security, integrity, and transparency while also maximizing the full benefits of the Blockchain.
FL works pari passu with IoT. The most recent pilots are in Business-to-Business (B2B) collaborations, where data nodes are smart devices or local nodes, as in edge computing. This sort of use case has continued to gain traction in the tech and Blockchain space, and that’s where PHB comes into the mix again.
FL & IoT presents many exciting opportunities, and PHB is set to benefit immensely by potentially onboarding consumers and their devices.
Final thoughts
The cons with IoT are undeniable, and so are the pros. Federated Learning offers a faultless and efficient ML mechanism to solve the existing concerns around hardware capacity, data privacy, and connectivity of IoT devices. Phoenix Global seeks to champion the Nex-Gen systems of extensive decentralized data with improved governance and transparency of AI and data.
