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Found 10 papers, 4 papers with code
DPBalance: Efficient and Fair Privacy Budget Scheduling for Federated Learning as a Service
We also theoretically prove the existence of a fairness-efficiency tradeoff in privacy budgeting.
Topology combined machine learning for consonant recognition
In artificial-intelligence-aided signal processing, existing deep learning models often exhibit a black-box structure, and their validity and comprehensibility remain elusive.
FedGKD: Unleashing the Power of Collaboration in Federated Graph Neural Networks
Federated training of Graph Neural Networks (GNN) has become popular in recent years due to its ability to perform graph-related tasks under data isolation scenarios while preserving data privacy.
Lumos: Heterogeneity-aware Federated Graph Learning over Decentralized Devices
In this paper, we propose the first federated GNN framework called Lumos that supports supervised and unsupervised learning with feature and degree protection on node-level federated graphs.
FedWalk: Communication Efficient Federated Unsupervised Node Embedding with Differential Privacy
FedWalk is designed to offer centralized competitive graph representation capability with data privacy protection and great communication efficiency.
Cost Effective MLaaS Federation: A Combinatorial Reinforcement Learning Approach
With the advancement of deep learning techniques, major cloud providers and niche machine learning service providers start to offer their cloud-based machine learning tools, also known as machine learning as a service (MLaaS), to the public.
Arbitrary Bit-width Network: A Joint Layer-Wise Quantization and Adaptive Inference Approach
We propose to feed different data samples with varying quantization schemes to achieve a data-dependent dynamic inference, at a fine-grained layer level.
Mixed-Precision Neural Network Quantization via Learned Layer-wise Importance
For example, MPQ search on ResNet18 with our indicators takes only 0. 06 s, which improves time efficiency exponentially compared to iterative search methods.
Parallel Spatio-Temporal Attention-Based TCN for Multivariate Time Series Prediction
As industrial systems become more complex and monitoring sensors for everything from surveillance to our health become more ubiquitous, multivariate time series prediction is taking an important place in the smooth-running of our society.
A Capsule Network for Traffic Speed Prediction in Complex Road Networks
Traffic flow data from induction loop sensors are essentially a time series, which is also spatially related to traffic in different road segments.
