Proposes MODIAD framework with MIS scheduling solved via SMG algorithm and REC-LoRA adaptation for efficient multimodal online distributed industrial anomaly detection, reporting superior performance on MVTec 3D-AD and Eyecandies datasets.
A survey on federated learning
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
citation-role summary
background 2
citation-polarity summary
years
2026 4verdicts
UNVERDICTED 4roles
background 2polarities
background 2representative citing papers
A model-agnostic framework combining GPBACC with robust aggregation and group testing for privacy-preserving and verifiable distributed learning in federated and decentralized settings.
The paper surveys technical requirements, use cases, challenges, and future trends for building brain-computer interfaces on top of 6G wireless networks.
Federated aggregation strategies show distinct performance trade-offs in accuracy, loss, and efficiency depending on whether client data distributions are homogeneous or heterogeneous.
citing papers explorer
-
Parameter Efficient Multi-Class Intelligent Scheduling for Multimodal Online Distributed Industrial Anomaly Detection
Proposes MODIAD framework with MIS scheduling solved via SMG algorithm and REC-LoRA adaptation for efficient multimodal online distributed industrial anomaly detection, reporting superior performance on MVTec 3D-AD and Eyecandies datasets.
-
Privacy-Preserving and Verifiable Approximate Distributed Coded Computing
A model-agnostic framework combining GPBACC with robust aggregation and group testing for privacy-preserving and verifiable distributed learning in federated and decentralized settings.
-
Toward 6G-enabled Brain Computer Interfaces: Technical Requirements, Use Cases, Challenges, and Future Trends
The paper surveys technical requirements, use cases, challenges, and future trends for building brain-computer interfaces on top of 6G wireless networks.
-
A Comparative Study of Federated Learning Aggregation Strategies under Homogeneous and Heterogeneous Data Distributions
Federated aggregation strategies show distinct performance trade-offs in accuracy, loss, and efficiency depending on whether client data distributions are homogeneous or heterogeneous.