Search Results for author:
Found 14 papers, 3 papers with code
BGTplanner: Maximizing Training Accuracy for Differentially Private Federated Recommenders via Strategic Privacy Budget Allocation
To mitigate the rising concern about privacy leakage, the federated recommender (FR) paradigm emerges, in which decentralized clients co-train the recommendation model without exposing their raw user-item rating data.
Federated Continual Graph Learning
To address these challenges, we present a pioneering study on Federated Continual Graph Learning (FCGL), which adapts GNNs to multiple evolving graphs within decentralized settings while adhering to storage and privacy constraints.
FedTAD: Topology-aware Data-free Knowledge Distillation for Subgraph Federated Learning
Subgraph federated learning (subgraph-FL) is a new distributed paradigm that facilitates the collaborative training of graph neural networks (GNNs) by multi-client subgraphs.
FedDWA: Personalized Federated Learning with Dynamic Weight Adjustment
In this paper, we propose a new PFL algorithm called \emph{FedDWA (Federated Learning with Dynamic Weight Adjustment)} to address the above problem, which leverages the parameter server (PS) to compute personalized aggregation weights based on collected models from clients.
BARA: Efficient Incentive Mechanism with Online Reward Budget Allocation in Cross-Silo Federated Learning
The challenge of this problem lies in the opaque feedback between reward budget allocation and model utility improvement of FL, making the optimal reward budget allocation complicated.
Edge-Based Video Analytics: A Survey
Edge computing has been getting a momentum with ever-increasing data at the edge of the network.
A2-FPN: Attention Aggregation Based Feature Pyramid Network for Instance Segmentation
Learning pyramidal feature representations is crucial for recognizing object instances at different scales.
A^2-FPN: Attention Aggregation based Feature Pyramid Network for Instance Segmentation
Learning pyramidal feature representations is crucial for recognizing object instances at different scales.
Virtual Reality: A Survey of Enabling Technologies and its Applications in IoT
Virtual Reality (VR) has shown great potential to revolutionize the market by providing users immersive experiences with freedom of movement.
Networking and Internet Architecture
Mitigate Parasitic Resistance in Resistive Crossbar-based Convolutional Neural Networks
We demonstrated the proposed methods with implementations of a 4-layer CNN on MNIST and ResNet(20, 32, and 56) on CIFAR-10.
Defects Mitigation in Resistive Crossbars for Analog Vector Matrix Multiplication
With storage and computation happening at the same place, computing in resistive crossbars minimizes data movement and avoids the memory bottleneck issue.
Memristor-based Deep Convolution Neural Network: A Case Study
In this paper, we firstly introduce a method to efficiently implement large-scale high-dimensional convolution with realistic memristor-based circuit components.
Long short-term memory networks in memristor crossbars
Recent breakthroughs in recurrent deep neural networks with long short-term memory (LSTM) units has led to major advances in artificial intelligence.
Emerging Technologies Applied Physics
Correlation of obesity and osteoporosis: Effect of free fatty acids on bone marrow-derived mesenchymal stem cell differentiation
In mouse BMSCs, the Sox9 and Runx2 genes were significantly up-regulated after exposure to CM from FFA-treated adipocytes, while PPARpparγ and CEBP-α were significantly down-regulated.
