LoaDiff is a diffusion-based generative model that produces year-long, sub-hourly smart‑meter electricity consumption time series. It can be conditioned on static household attributes like appliance ownership and dynamic factors such as calendar dates and outdoor temperature. Evaluations on three residential datasets show that LoaDiff generates realistic, diverse load profiles, limits memorization, retains useful information for downstream tasks, and responds coherently to conditioning changes.
By Mariia Baranova, Adrien Petralia, Etienne Le Naour, Nathan Etourneau, Guillaume Hofmann, Themis Palpanas
arXiv:2610.08538v1 Announce Type: cross
Abstract: Probabilistic load forecasting has been widely studied for power-system operation and planning, but customer- and transformer-level forecasting intro...
By Haoran Li, Zhe Cheng, Yang Weng
arXiv:2606. 11272v1 Announce Type: cross Abstract: Federated Learning (FL) enables collaborative and privacy-preserving model training across distributed clients, but most existing FL systems implicitly assume data stationarity.
By Masoume Gholizade, Fabrizio Ruffini, Pietro Ducange, Francesco Marcelloni
arXiv:2512. 24625v3 Announce Type: replace-cross Abstract: Accurate traffic prediction is essential for Intelligent Transportation Systems, including ride-hailing, urban road planning, and vehicle fleet management.
By Zijian Zhao, Yitong Shang, Sen Li
The paper proposes an opinion‑dynamics approach to form client coalitions in federated learning for heterogeneous IoT systems, specifically targeting smart‑meter water‑consumption forecasting. By modeling coalition formation as a Hegselmann‑Krause bounded‑confidence process on local model weights, the method creates stable coalition structures within ten iterations without extra client computation or communication. Experiments on real smart‑meter data show the HK‑based coalitions reduce mean absolute error by up to 54% compared to FedAvg and achieve the highest global accuracy (83‑85%).
The paper proposes an opinion‑dynamics approach to form client coalitions in federated learning for heterogeneous IoT systems, specifically targeting smart‑meter water‑consumption forecasting. By modeling coalition formation as a Hegselmann‑Krause bounded‑confidence process on local model weights, the method creates stable coalition structures within ten iterations without extra client computation or communication. Experiments on a real smart‑metering dataset show that this HK‑based coalition formation reduces mean absolute error by up to 54% compared to FedAvg and achieves the highest global accuracy (83‑85%).
By Mohammed El Hanjri, Anas Abouaomar, Hamidou Tembine, Abdellatif Kobbane
arXiv:2608. 15107v1 Announce Type: new Abstract: Federated learning (FL) is a practical framework that can train models on distributed user data while guaranteeing data privacy; however, due to heterogeneity in which each user has a different data distribution, problems frequently arise where both global and personalization performance deteriorate simultaneously.
By Seongyoon Kim
arXiv:2602. 06838v3 Announce Type: replace Abstract: Federated learning enables collaborative model training across distributed clients while preserving data privacy.
By Jin Wang, Hui Ma, Yajun Zhang, Xinjun Pei, Ming Yan, Fei Xing, Yikun Chen
The paper introduces a black-box defense strategy for smart meter data that uses a proxy-guided hierarchical reinforcement learning framework to generate battery-based load-shaping policies. These policies inject realistic yet misleading appliance-level signatures into aggregate power signals, disrupting non-intrusive load monitoring attacks. Experiments on UK-DALE and REDD datasets show significant increases in appliance-level reconstruction error and reductions in attacker F1 scores across multiple unseen NILM models.
By Ruichang Zhang, Mustafa A. Mustafa
Federated adaptation of time-series foundation models (TSFMs) is attractive for building energy forecasting because meter data are private, distributed, and highly non-IID. However, a single parameter-sharing strategy is unlikely to serve all pretrained TSFMs or building clients: fully shared adapters can suppress building-specific temporal behavior, while fully local adaptation discards cross-building transfer.
arXiv:2608. 14654v1 Announce Type: cross Abstract: Federated Learning (FL) is a collaborative paradigm that enables multiple devices to train a global model while preserving local data privacy.
By Hai Anh Tran, Cuong Ta, Truong X. Tran
arXiv:2609.39250v1 Announce Type: new
Abstract: Federated learning (FL) is a promising paradigm of machine learning, which preserves user privacy by enabling learning without sharing raw data with a...
By Muzaffer Citir, Hiroki Nishikawa, Sangyoung Park