Federated Learning Revolutionizes Distributed Systems: Adaptive AI-Driven Multi-Cloud Load Balancing Solutions
Keywords:
federated learning, distributed systems, load balancing, multi-cloud environmentsAbstract
Modern computing requires multi-cloud distributed systems for scalability, reliability, and cost optimization. Workload changes, resource setups, and real-time adaption challenge dynamic load balancing. Decentralized machine learning (FL) solves these problems using AI models trained on several nodes without data centralization. FL's AI-powered load balancing may alter dispersed networks. Performance optimization case studies, heterogeneous cloud load distribution, and FL architecture analysis are important. FL implementation security, scalability, and communication overhead are addressed. FL may enhance latency, resource utilization, and system reliability in dynamic clouds.
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References
Madupati, Bhanuprakash. "Leveraging AI for Rural Development." Available at SSRN 5135045 (2024).
Madupati, Bhanuprakash. "Cyber Attacks in the Remote Work Era: An Analysis of Phishing, Ransomware, and Mitigation Strategies." Ransomware, and Mitigation Strategies (September 30, 2024) (2024).
Kalluri, Kartheek. "Scalable fine-tunning strategies for llms in finance domain-specific application for credit union." 2024,
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.
Kasula, Vinay Kumar, et al. "Enhancing Vulnerability Detection in Smart Contracts Using Transformer-Based Embeddings and Graph Neural Networks." 2024 34th International Conference on Computer Theory and Applications (ICCTA). IEEE, 2024.
Nair, Sreejith Sreekandan, et al. "Safeguarding Tomorrow-Fortifying Child Safety in Digital Landscape." 2024 International Conference on Computing, Sciences and Communications (ICCSC). IEEE, 2024.
Madupati, Bhanuprakash. "The Role of AI in the Public Sector: A Technical Perspective." Available at SSRN 5076600 (2024).
Kalluri, K. "AI-Driven Risk Assessment Model for Financial Fraud Detection: a Data Science Perspective." International Journal of Scientific Research and Management 12.12 (2024): 1764-1774.
Kasula, Vinay Kumar, et al. "Enhancing Smart Contract Vulnerability Detection using Graph-Based Deep Learning Approaches." 2024 International Conference on Integrated Intelligence and Communication Systems (ICIICS). IEEE, 2024.
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.
Kalluri, Kartheek. "Low-Code BPM meets IoT: A Framework for Real-Time Industrial Automation." 2024,
Vududala, Santosh Kumar. "Enhancing Anti-Money Laundering (AML) Compliance using NICE Actimize: A Data driven Approach."
Madupati, Bhanuprakash. "AI-Driven Threat Detection in Cybersecurity." Available at SSRN 5076610 (2024).
Konda, Bhargavi, et al. "A Public Key Searchable Encryption Scheme Based on Blockchain Using Random Forest Method." International Journal Of Research In Electronics And Computer Engineering 12.1 (2024): 77-83.
Kalluri, Kartheek. "Integrating Pega's AI-Driven Workflows for End-to-End Process Optimization in Financial Services." North American Journal of Engineering Research 5.3 (2024).
Vududala, Santosh Kumar. "International Journal of Multidisciplinary Research and Growth Evaluation."
Madupati, Bhanuprakash. "The Role of Cybersecurity in Combating Digital Crime-A Technical Perspective." Available at SSRN 5076618 (2024).
Kasula, Vinay Kumar, et al. "Fortifying cloud environments against data breaches: A novel AI-driven security framework." World J. Adv. Res. Rev 24 (2024): 1613-1626.
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