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Collaboration-based personalized federated learning (Co-PFL) mitigates data heterogeneity by enabling clients to selectively exchange knowledge through a learned collaboration graph. Despite their success, existing Co-PFL methods often update collaboration weights mainly from pairwise similarity, which can produce grap...
Mobility-aware hierarchical federated learning (MobiHFL) is an emerging paradigm to support urban applications where clients continuously move across edge-server coverage areas. Existing methods mainly treat mobility as a scheduling challenge and often train a single global model across all edge-servers. This overlooks...
Retrieval-Augmented Generation (RAG) is a promising technique for applying LLMs to proprietary domains. However, retrieved documents may contain sensitive knowledge, posing risks of privacy leakage in generative results. Thus, effectively erasing private information from retrieved documents is a key challenge for RAG....
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