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Research Topics in Federated Learning Model with Potential Applications

Research Topics in Federated Learning Model with Potential Applications

Latest Research Topics in Federated Learning Model with Potential Applications

  •  Federated Learning (FL) is the new efflorescing technology of Artificial Intelligence (AI) that deals with privacy-preserving aspects in distributed scenery-based applications. Federated learning mainly focuses on collaborative decentralized and distributed learning frameworks. Federated learning utilizes machine learning and deep learning models that train cooperatively with heterogeneous devices having different constraints. The impressive significance of the federated learning concept is privacy and security concerns, real-time predictions, infrastructure efficiency, and data diversification. The core classification of federated learning is Horizontal Federated Learning (HFL), Vertical Federated Learning (VFL), Federated Transfer Learning (FTL), cross-silo federated learning, and cross-device federated learning.The applicability of federated learning is explored in different domains such as

Healthcare and Biomedical: Wearable healthcare, collaborative drug discovery, health decision-based clinical data, brain segmentation, and electronic health records maintenance.

Edge computing and Internet of Things (IoT): Network and edge device anomaly detection, Mobile keyboard prediction, virtual reality, human activity recognition, and mobile device information tracking.

Industrial management: Sensor failure prediction, Industrial IoT, and Environment condition modeling.

Recommender systems: Privacy-preserving recommender system, Graph model, Meta-learning, Reinforcement learning-based recommendation system, and Smart cities

Autonomous Industry: Security monitoring, Traffic sign recognition, Vehicle scheduling routing, and traffic flow prediction.

Physical information system: Autonomous driving, Cloud robotics systems, Failure prediction in aeronautics, and Place patching.

Natural Language Processing (NLP): Language modeling, Sequence tagging, Classification and labeling, Translation and summarization.

Banking and finance: Anti-financial crime process, Financial text recognition, Credit card fraud detection, Loan risk prediction, and Open banking.