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SecFD-FL: Frequency-Decoupled Federated Learning with Secure Aggregation for Charging Load Forecasting in Internet of Electric Vehicles
US & West ·
arXiv:2610.12909v1 Announce Type: new Abstract: Accurate charging load forecasting is essential for the reliability and efficiency of the Internet of Electric Vehicles (IoEV), yet it is challenging on two fronts: charging demand is highly stochastic, and the data collected by distributed sensors is…
Original headline
SecFD-FL: Frequency-Decoupled Federated Learning with Secure Aggregation for Charging Load Forecasting in Internet of Electric Vehicles
Gasification of Bishkek residential areas will continue from 2028, according to the Kyrgyz parliament speaker citing Gazprom's local subsidiary. Completion of a pipeline from Russia is named as the condition.
arXiv:2610.07232v1 Announce Type: cross Abstract: Accurate short-term load forecasting (STLF) is essential for the reliable and efficient operation of modern power systems. While time series foundation models (TSFMs) have recently demonstrated remarkable performance across a wide range of…
arXiv:2605.12728v2 Announce Type: replace Abstract: The power distribution engineering workforce faces a projected shortage of up to 1.5 million engineers by 2030, creating urgent demand for more accessible analysis tools.
