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Found 18 papers, 5 papers with code
From RNNs to Foundation Models: An Empirical Study on Commercial Building Energy Consumption
The impact of increasing dataset heterogeneity in time series forecasting, while keeping size and model constant, is understudied.
FedSpaLLM: Federated Pruning of Large Language Models
Large Language Models (LLMs) achieve state-of-the-art performance but are challenging to deploy due to their high computational and storage demands.
Advances in APPFL: A Comprehensive and Extensible Federated Learning Framework
Federated learning (FL) is a distributed machine learning paradigm enabling collaborative model training while preserving data privacy.
Foundation Models for the Electric Power Grid
Foundation models (FMs) currently dominate news headlines.
Asynchronous Federated Stochastic Optimization for Heterogeneous Objectives Under Arbitrary Delays
Federated learning (FL) was recently proposed to securely train models with data held over multiple locations ("clients") under the coordination of a central server.
Addressing Heterogeneity in Federated Load Forecasting with Personalization Layers
The advent of smart meters has enabled pervasive collection of energy consumption data for training short-term load forecasting models.
SparseLLM: Towards Global Pruning for Pre-trained Language Models
The transformative impact of large language models (LLMs) like LLaMA and GPT on natural language processing is countered by their prohibitive computational demands.
Secure Federated Learning Across Heterogeneous Cloud and High-Performance Computing Resources -- A Case Study on Federated Fine-tuning of LLaMA 2
Federated learning enables multiple data owners to collaboratively train robust machine learning models without transferring large or sensitive local datasets by only sharing the parameters of the locally trained models.
QCQP-Net: Reliably Learning Feasible Alternating Current Optimal Power Flow Solutions Under Constraints
At the heart of power system operations, alternating current optimal power flow (ACOPF) studies the generation of electric power in the most economical way under network-wide load requirement, and can be formulated as a highly structured non-convex quadratically constrained quadratic program (QCQP).
Privacy-Preserving Load Forecasting via Personalized Model Obfuscation
The widespread adoption of smart meters provides access to detailed and localized load consumption data, suitable for training building-level load forecasting models.
FedCompass: Efficient Cross-Silo Federated Learning on Heterogeneous Client Devices using a Computing Power Aware Scheduler
Nonetheless, because of the disparity of computing resources among different clients (i. e., device heterogeneity), synchronous federated learning algorithms suffer from degraded efficiency when waiting for straggler clients.
Federated Short-Term Load Forecasting with Personalization Layers for Heterogeneous Clients
The advent of smart meters has enabled pervasive collection of energy consumption data for training short-term load forecasting (STLF) models.
APPFLx: Providing Privacy-Preserving Cross-Silo Federated Learning as a Service
Cross-silo privacy-preserving federated learning (PPFL) is a powerful tool to collaboratively train robust and generalized machine learning (ML) models without sharing sensitive (e. g., healthcare of financial) local data.
Differentially Private Distributed Convex Optimization
This paper considers distributed optimization (DO) where multiple agents cooperate to minimize a global objective function, expressed as a sum of local objectives, subject to some constraints.
Differentially Private Federated Learning via Inexact ADMM with Multiple Local Updates
Differential privacy (DP) techniques can be applied to the federated learning model to statistically guarantee data privacy against inference attacks to communication among the learning agents.
APPFL: Open-Source Software Framework for Privacy-Preserving Federated Learning
Federated learning (FL) enables training models at different sites and updating the weights from the training instead of transferring data to a central location and training as in classical machine learning.
A Reinforcement Learning Approach to Parameter Selection for Distributed Optimal Power Flow
We train our RL policy using deep Q-learning, and show that this policy can result in significantly accelerated convergence (up to a 59% reduction in the number of iterations compared to existing, curvature-informed penalty parameter selection methods).
Differentially Private Federated Learning via Inexact ADMM
Differential privacy (DP) techniques can be applied to the federated learning model to protect data privacy against inference attacks to communication among the learning agents.
