International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

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Enhancing Collaborative Intelligence: Synergistic Approaches for Distributed Machine Learning and Ai Collaboration in Heterogeneous Environments

Author(s) Vidya Chandgude, Priti Zambare, Kavita Mahajan
Country India
Abstract Collaborative intelligence is vital in distributed machine learning and AI collaboration, especially in heterogeneous environments. This paper explores synergistic approaches to enhance collaborative intelligence by addressing challenges in communication, privacy, resource optimization, domain adaptation, and scalability. The paper reviews existing techniques and methodologies in the field of collaborative intelligence. It discusses protocols, coordination strategies, and communication mechanisms for effective collaboration. Privacy-preserving techniques, such as federated learning and secure multi-party computation, are examined. Resource optimization techniques, including load balancing and adaptive resource allocation, are explored. Domain adaptation and transfer learning methods are also discussed.
Keywords Collaborative intelligence, Heterogeneous Environments, Distributed Machine Learning
Field Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In Volume 6, Issue 1, January-February 2024
Published On 2024-02-29
Cite This Enhancing Collaborative Intelligence: Synergistic Approaches for Distributed Machine Learning and Ai Collaboration in Heterogeneous Environments - Vidya Chandgude, Priti Zambare, Kavita Mahajan - IJFMR Volume 6, Issue 1, January-February 2024. DOI 10.36948/ijfmr.2024.v06i01.13704
DOI https://doi.org/10.36948/ijfmr.2024.v06i01.13704
Short DOI https://doi.org/gtktjj
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