Decentralized Inference

Decentralized inference focuses on performing machine learning inference across distributed networks of devices, aiming to improve scalability, privacy, and robustness compared to centralized approaches. Current research emphasizes efficient algorithms and model architectures, such as graph neural networks and sharded deep learning models, often incorporating techniques like compression and privacy-preserving mechanisms to mitigate communication and security challenges. This area is significant for enabling the deployment of large-scale AI systems in resource-constrained environments and fostering collaborative learning while protecting sensitive data, with applications ranging from sensor networks to multi-agent systems and edge computing.