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Found 8 papers, 2 papers with code
SegRNN: Segment Recurrent Neural Network for Long-Term Time Series Forecasting
To address these issues, we propose two novel strategies to reduce the number of iterations in RNNs for LTSF tasks: Segment-wise Iterations and Parallel Multi-step Forecasting (PMF).
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Time Series Forecasting
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PETformer: Long-term Time Series Forecasting via Placeholder-enhanced Transformer
Recently, the superiority of Transformer for long-term time series forecasting (LTSF) tasks has been challenged, particularly since recent work has shown that simple models can outperform numerous Transformer-based approaches.
Variation-Incentive Loss Re-weighting for Regression Analysis on Biased Data
Both classification and regression tasks are susceptible to the biased distribution of training data.
FedProf: Selective Federated Learning with Representation Profiling
The results show that the selective behaviour of our algorithm leads to a significant reduction in the number of communication rounds and the amount of time (up to 2. 4x speedup) for the global model to converge and also provides accuracy gain.
An Efficiency-boosting Client Selection Scheme for Federated Learning with Fairness Guarantee
The client selection policy is critical to an FL process in terms of training efficiency, the final model's quality as well as fairness.
Accelerating Federated Learning over Reliability-Agnostic Clients in Mobile Edge Computing Systems
In this paper, a multi-layer federated learning protocol called HybridFL is designed for the MEC architecture.
SAFA: a Semi-Asynchronous Protocol for Fast Federated Learning with Low Overhead
Federated learning (FL) has attracted increasing attention as a promising approach to driving a vast number of end devices with artificial intelligence.
Developing an Unsupervised Real-time Anomaly Detection Scheme for Time Series with Multi-seasonality
In light of this, we have developed a prediction-driven, unsupervised anomaly detection scheme, which adopts a backbone model combining the decomposition and the inference of time series data.
