STAGE builds a shared semantic space through feature translation and controlled graph propagation to reduce semantic drift in multimodal federated graph learning, delivering state-of-the-art results with lower communication cost.
Communication-efficient learning of deep networks from decentralized data
4 Pith papers cite this work. Polarity classification is still indexing.
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Proactive client selection in federated learning via differentially private mutual information and simulated annealing to optimize Potential Federation Loss for utility and fairness.
Federated learning with 1DCNN on RF IQ samples detects 5G jamming attacks at 97% accuracy without sharing user data.
A system architecture combines GenAI with typed argument graphs, RAG, and deterministic validation rules to generate traceable, evidence-supported formal arguments for regulatory compliance.
citing papers explorer
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STAGE: Tackling Semantic Drift in Multimodal Federated Graph Learning
STAGE builds a shared semantic space through feature translation and controlled graph propagation to reduce semantic drift in multimodal federated graph learning, delivering state-of-the-art results with lower communication cost.
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Choose Wisely and Privately: Proactive Client Selection for Fair and Efficient Federated Learning
Proactive client selection in federated learning via differentially private mutual information and simulated annealing to optimize Potential Federation Loss for utility and fairness.
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Toward Resilient 5G Networks: Comparative Analysis of Federated and Centralized Learning for RF Jamming Detection
Federated learning with 1DCNN on RF IQ samples detects 5G jamming attacks at 97% accuracy without sharing user data.
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Compliance-by-Construction Argument Graphs: Using Generative AI to Produce Evidence-Linked Formal Arguments for Certification-Grade Accountability
A system architecture combines GenAI with typed argument graphs, RAG, and deterministic validation rules to generate traceable, evidence-supported formal arguments for regulatory compliance.