How can I efficiently unlearn data in federated drone learning?
Sky of Unlearning (SOUL): Rewiring Federated Machine Unlearning via Selective Pruning
This paper introduces SoUL (Sky of Unlearning), a system for removing specific data from a federated learning model trained by a network of drones (Internet of Drones or IoD). This addresses privacy concerns and allows for the removal of malicious data without retraining the entire model. A key component is a selective pruning algorithm that removes only the parts of the neural network most affected by the unwanted data, preserving accuracy and efficiency.
The selective pruning algorithm enhances efficiency and reduces communication overhead, crucial aspects for LLM-based multi-agent systems. The decentralized nature of federated learning mirrors the distributed nature of multi-agent systems, making SoUL's approach relevant for managing updates and removing data from LLMs operating in a multi-agent environment. The focus on minimizing communication overhead is particularly pertinent to large language models due to their size and computational demands.