Constructing Executing and Overcoming Challenges in Distributed AI Systems: A Study of Federated Learning Framework DOI Creative Commons

José Gabriel Carrasco Ramírez

Deleted Journal, Journal Year: 2024, Volume and Issue: 3(1), P. 197 - 216

Published: April 2, 2024

Federated learning stands out as a promising approach within the realm of distributed artificial intelligence (AI) systems, facilitating collaborative model training across decentralized devices while safeguarding data privacy. This study presents thorough investigation into federated architecture, covering its foundational design principles, implementation methodologies, and significant challenges encountered in AI systems. We delve fundamental mechanisms underpinning learning, elucidating merits diverse environments prospective applications various domains. Additionally, we scrutinize technical complexities associated with deploying including considerations such communication efficiency, aggregation techniques, security protocols. By amalgamating insights gleaned from recent research endeavors practical deployments, this furnishes valuable guidance for both researchers practitioners aiming to harness development scalable privacy-preserving solutions.

Language: Английский

Navigating the Terrain: Scaling Challenges and Opportunities in AI/ML Infrastructure DOI Creative Commons

José Gabriel Carrasco Ramírez,

Md.Mafiqul Islam

Deleted Journal, Journal Year: 2024, Volume and Issue: 2(1), P. 209 - 228

Published: March 30, 2024

Navigating the complexities of scaling AI/ML infrastructure unveils a terrain rife with challenges and opportunities. This exploration delves into multifaceted landscape, addressing key aspects such as resource expansion, data management, parallel processing, algorithmic optimization, orchestration, monitoring, streamlined pipelines, automation, financial considerations, security. By embracing innovation resilience, organizations can effectively harness potential AI ML technologies while mitigating scalability hurdles.

Language: Английский

Citations

0
Machine Learning Algorithms Scaling on Large-Scale Data Infrastructure DOI Creative Commons

Harish Padmanaban

Deleted Journal, Journal Year: 2024, Volume and Issue: 3(1), P. 1 - 26

Published: April 2, 2024

Scalability is a critical aspect of deploying machine learning (ML) algorithms on large-scale data infrastructure. As datasets grow in size and complexity, organizations face challenges efficiently processing analyzing to derive meaningful insights. This paper explores the strategies techniques employed scale ML effectively extensive From optimizing computational resources implementing parallel frameworks, various approaches are examined ensure seamless integration models with systems.

Language: Английский

Citations

0
Dynamic Resource Allocation for AI/ML Applications in Edge Computing: Framework Architecture and Optimization Methods DOI Creative Commons

Md. Mafiqul Islam

Deleted Journal, Journal Year: 2024, Volume and Issue: 3(1), P. 51 - 65

Published: April 4, 2024

This scholarly paper introduces an extensive architectural framework and optimization strategies designed specifically for dynamic resource allocation in edge computing environments, with a focus on AI/ML applications. The rise of presents viable solution managing the computational complexities tasks by utilizing resources proximity to data sources. Nevertheless, effective encounters significant hurdles due diverse ever-changing nature environments. In addressing these challenges, innovative that integrates methodologies unique requirements encompasses range techniques customized efficiently distribute resources, taking into account factors such as workload attributes, availability, latency limitations. Through simulations evaluations, study showcases effectiveness proposed approach enhancing utilization, reducing latency, bolstering overall performance workloads within scenarios.

Language: Английский

Citations

0
Balancing Increased Demand with Grid Security: Addressing Vulnerabilities in Real-Time Energy Systems DOI

Md Khaledur Rahman

SSRN Electronic Journal, Journal Year: 2024, Volume and Issue: unknown

Published: Jan. 1, 2024

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Language: Английский

Citations

0
Constructing Executing and Overcoming Challenges in Distributed AI Systems: A Study of Federated Learning Framework DOI Creative Commons

José Gabriel Carrasco Ramírez

Deleted Journal, Journal Year: 2024, Volume and Issue: 3(1), P. 197 - 216

Published: April 2, 2024

Federated learning stands out as a promising approach within the realm of distributed artificial intelligence (AI) systems, facilitating collaborative model training across decentralized devices while safeguarding data privacy. This study presents thorough investigation into federated architecture, covering its foundational design principles, implementation methodologies, and significant challenges encountered in AI systems. We delve fundamental mechanisms underpinning learning, elucidating merits diverse environments prospective applications various domains. Additionally, we scrutinize technical complexities associated with deploying including considerations such communication efficiency, aggregation techniques, security protocols. By amalgamating insights gleaned from recent research endeavors practical deployments, this furnishes valuable guidance for both researchers practitioners aiming to harness development scalable privacy-preserving solutions.

Language: Английский

Citations

0