Federation of AI Frameworks for Dynamic Smart Device Data Sharing Privacy
Keywords:
Federated AI, privacy preservation, smart devices, data sharingAbstract
The IoT and smart device era requires collecting and sharing sensitive data. Broad data exchange harms privacy, particularly personal data. FedAI frameworks overcome these concerns via privacy-preserving data sharing and computation. FedAI's role in smart device data sharing's dynamic privacy is investigated. We study how FL and other federated AI approaches may preserve privacy and enable device machine learning cooperation. The paper covers real-world implementations, privacy, security, scalability, and federated AI's privacy-preserving data exchange. Research suggests federated AI frameworks may build secure, efficient, and privacy-respecting smart device network systems.
Downloads
References
Madupati, Bhanuprakash. "Comprehensive Approaches to API Security and Management in Large-Scale Microservices Environments." Available at SSRN 5076630 (2023).
Yadulla, A. R., et al. "A time-aware LSTM model for detecting criminal activities in blockchain transactions." International Journal of Communication and Information Technology 4.2 (2023): 33-39.
Shankeshi, Raghu M. "IoT data management in Oracle databases: Challenges and solutions." International Journal For Multidisciplinary Research 4 (2022): 1-18.
Kalluri, Kartheek. "Exploring Zero-Shot and Few-Shot Learning Capabilities in LLMS for Complex Query Handling." 2022,
Vududala, Santosh Kumar. "QA role and challenges in Robotics Automation Testing."
Shankeshi, Raghu Murthy. "Optimizing IoT Data Pipelines Using Oracle Autonomous Databases and AI Analytics." American Journal of Autonomous Systems and Robotics Engineering 3 (2023): 35-56.
Madupati, Bhanuprakash. "Serverless Architectures and Function-As-A-Service (Faas): Scalability, Cost Efficiency, And Security Challenges." Cost Efficiency, And Security Challenges (April 05, 2023) (2023).
Yadulla, Akhila Reddy, et al. "Enhancing Cybersecurity with AI: Implementing a Deep Learning-Based Intrusion Detection System Using Convolutional Neural Networks." European Journal of Advances in Engineering and Technology 10.12 (2023): 89-98.
Vududala, Santosh Kumar. "International Journal of Multidisciplinary Research and Growth Evaluation."
Shankeshi, Raghu Murthy. "The Role of AI in Enhancing Data Security and Compliance in Oracle Cloud Infrastructures." American Journal of Data Science and Artificial Intelligence Innovations 3 (2023): 53-67.
Madupati, Bhanuprakash. "Kubernetes for Multi-Cloud and Hybrid Cloud: Orchestration, Scaling, and Security Challenges." Scaling, and Security Challenges (June 30, 2023) (2023).
Kalluri, Kartheek. "Enhancing Credit Union Operations: Utilizing Pega's Workflow Automation for Member Management." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 7 (2023): 1-7.
Madupati, Bhanuprakash. "Observability in Microservices Architectures: Leveraging Logging, Metrics, and Distributed Tracing in Large-Scale Systems." Metrics, and Distributed Tracing in Large-Scale Systems (November 30, 2023) (2023).
Yenugula, Mounica, et al. "Enhancing Mobile Data Security with Zero-Trust Architecture and Federated Learning: A Comprehensive Approach to Prevent Data Leakage on Smart Terminals." JOURNAL OF RECENT TRENDS IN COMPUTER SCIENCE AND ENGINEERING (JRTCSE) 11.1 (2023): 52-64.
Kalluri, Kartheek. "Adapting LLMs for Low Resource Languages-Techniques and Ethical Considerations." (2023).
Madupati, Bhanuprakash. "Kubernetes for Multi-Cloud and Hybrid Cloud: Orchestration, Scaling, and Security Challenges." Scaling, and Security Challenges (June 30, 2023) (2023).
Kalluri, Kartheek. "Assessing the Impact of Pega's Robotic Process Automation on Supply Chain Management Efficiency." Methodology 7.06 (2023).
Downloads
Published
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
