Applications of Federated Learning and Artificial Intelligence

1. Applications of Federated Learning and Artificial Intelligence Artificial Intelligence is an advancing technology that will continue to evolve and gain massive traction in today’s world. With AI, machines can now exhibit human-level intelligence. Simply put, FL is a decentralized AI that integrates well with the blockchain domain. Phoenix Global is pioneering blockchain-based uses of FL combined with the existing applications of AI. https://phoenix.global/ Artificial Intelligence (AI) - CrossPlatform Applications Marketing AI for CRM An AI-powered CRM gives businesses real-time insights across several consumer channels. AI-CRM can also learn from historical patterns to determine the best sales leads for the future. Finance, Market Analysis, and Predictions AI-powered machines can crunch large volumes of data, learn market patterns from past data, and provide futuristic predictions. Automotive The data collected in the automotive industry is massive. AI comes to play, particularly with self-driving vehicles and robotics. Healthcare AI can help health professionals make the right calls in emergency scenarios with stroke prevention systems, cancer diagnostic systems, and real-time health trackers among the few. Banking Several banking systems have already used AI-oriented solutions to detect fraud, solve credit card anomalies, and improve consumer support. Machine Learning and Artificial Intelligence birthed intelligent chatbots, providing real-time and relevant responses with excellent speech recognition. Other Artificial Intelligence and Federated Learning applications offered by Phoenix Global include retail, travel, consumer internet, luxury, & lifestyle. FL Techniques, Phoenix Global, and the Link with AI Federated Learning brings a new methodology to AI and promises transformative potential for several ecosystems. Phoenix Global actively engages strategic partners to integrate existing artificial intelligence models and systems, such as APEX IQ. FL opens up a massive pool of data sets, which are domiciled on individual data hosts, to learning algorithms. Phoenix Oracle integration is also in the pipeline, which should initiate fast, reliable connections with existing AI platforms. This technique preserves data privacy regulations, as the system does not share information put forward by the participants during the learning process. Final thoughts Federated Learning (FL) techniques usher in a new ML paradigm that trains algorithms locally and aggregates learning algorithms at a centralized server. This technique is capable of avoiding direct data sharing and reducing critical data leakages. FL also creates a decentralized AI system that supersedes the traditional AI methodology. BLOCKCHAIN POWERING THE NEXT GENERATION OF CONSUMER APPLICATIONS More Private.More Interactive.More Connected. twitter.com/Phoenix_Chain phoenix.global t.me/APEXcommunity github.com/phoenixglobal phoenixglobal.medium.com www.reddit.com/r/PhoenixGlobal