AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
FedRAHi, a Reliability-Aware Hierarchical collaboration framework for FedGFM that leverages the symbiotic knowledge to construct a client-aware affinity graph, and performs personalized weighting of client parameters based on the reliability scores is proposed.
FedDUA is proposed, a novel disagreement-aware and uncertainty-guided framework for subgraph FL that first models cross-client semantic disagreement via a lightweight semantic anchor graph and derives adaptive aggregation weights for reliable global federated knowledge.
In this work, we investigate the task of Federated Generalized Category Discovery (Fed-GCD), which aims to leverage labeled data from known classes to cluster unlabeled samples from both known and unknown classes through privacy-preserving collaboration among clients. Existing Fed-GCD methods predominantly emphasize im...
The rapid growth of multi-source, multi-perspective data in healthcare, finance, and social media has increased the need for unsupervised learning methods that integrate diverse views while preserving data privacy and locality. Federated learning (FL) enables collaborative model training without sharing raw data, yet i...
Mixture-of-Experts (MoE) has become a widely adopted architecture for Large Language Models (LLMs), as it improves model capacity while limiting computational overhead through sparse expert activation. This property makes MoE-based LLMs particularly attractive for resource-constrained distributed environments. However,...
PMFRec learns a personalized representation generator to produce user-specific item representations from attribute features, and introduces a global multi-view encoder with item-adaptive gating and an orthogonality objective to capture complementary semantic views while reducing cross-view redundancy.
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