ROSS: RObust decentralized Stochastic learning based on Shapley values
PositiveArtificial Intelligence
- A new decentralized learning algorithm named ROSS has been proposed, which utilizes Shapley values to enhance the robustness of stochastic learning among agents. This approach addresses challenges posed by heterogeneous data distributions, allowing agents to collaboratively learn a global model without a central server. Each agent updates its model by aggregating cross-gradient information from neighboring agents, weighted by their contributions.
- The introduction of ROSS is significant as it represents a step forward in decentralized learning, particularly in environments where data is non-independent and potentially compromised. By leveraging Shapley values, the algorithm aims to improve the efficiency and reliability of collaborative learning among agents, which is crucial for real-world applications.
- This development highlights a growing trend in artificial intelligence towards enhancing collaborative capabilities among agents, moving beyond mere task completion to fostering ongoing interaction and adaptation. The focus on Shapley values also aligns with broader discussions in the field regarding the need for explainability and fairness in AI systems, as researchers seek to address the complexities of decentralized environments.
— via World Pulse Now AI Editorial System
