How can I improve federated learning generalization without sharing data?
FEDERATED DOMAIN GENERALIZATION WITH DATA-FREE ON-SERVER GRADIENT MATCHING
This paper introduces FedOMG (Federated Learning via On-server Matching Gradient), a new approach for training machine learning models across multiple devices (clients) without sharing their private data. It focuses on the problem of federated domain generalization, where each client's data may have different characteristics (different domains), making it difficult for the model to generalize to unseen data. FedOMG addresses this by using a clever trick: instead of sharing data, it uses the gradients calculated on each client during training. It then uses an optimization process on a central server to find a combination of these gradients that leads to a model that works well across all domains.
For LLM-based multi-agent systems, this research is relevant because it tackles the problem of domain generalization in a decentralized setting. This is crucial for multi-agent applications where agents might be trained on diverse data but need to collaborate effectively. The gradient-matching approach could help create more robust LLMs that are less sensitive to variations in the data they are trained on, enabling better generalization and collaboration in diverse multi-agent environments. The on-server optimization aspect is also relevant, as it minimizes communication overhead, which is often a concern in distributed multi-agent systems.