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

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.

• Publication year

  2024

• Venue

  International Journal For Multidisciplinary Research

• Publication date

  2024-02-29

• Fields of study

  Not labeled

• Identifiers
  DOI 10.36948/ijfmr.2024.v06i01.13704
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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