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...
Overall, FedMVC represents a promising framework for privacy-preserving unsupervised learning in distributed environments and key research challenges include formal privacy–utility trade-offs, convergence under extreme data heterogeneity, and the integration of large language models for improved interpretability.
FedTAR is proposed, a task-aware federated fine-tuning method for MoE-based LLMs that establishes the association between local updates and task preference via routing outputs and preserves expert specialization and mitigates destructive interference among heterogeneous clients.
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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