FedMVLA is proposed, a modality-decoupled FL framework for privacy-preserving embodied intelligence in 6G networks that incorporates three mechanisms: modality-aware federated aggregation (MAFA), modality-aware privacy allocation (MAPA), and modality-aware communication compression (MACO), together with a modality-slic...
Experiments show that \textsc{AtomRec} consistently outperforms state-of-the-art agentic and memory-augmented baselines, with around 8.5\% average relative improvement across metrics.
Pei-Yu Hu, Wei-Hai Lu, Si-Ying Gu et al.· 0 citations
FedCGR is proposed, a federated generative CDR framework that keeps the item language stable and makes adaptation explicit and consistently outperforms federated generative baselines and achieves competitive performance against strong sequential and federated CDR methods under both full-ranking and sampled evaluation p...
Zhuo-Dong Liu, Hugen Lv, Xiang-Yu Li et al.· 0 citations
To further optimize the reasoning trajectory, HiLaR combines final recommendation feedback with layer-aware process rewards derived from the marginal target-likelihood gain of each state, and generally outperforms strong sequential, generative, and LLM-based recommendation baselines.
Pei-Yu Hu, Si-Ying Gu, Wei-Hai Lu et al.· arXiv.org· 1 citation
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