TL;DR
The results establish that privacy-preserving distributed learning can achieve the accuracy requirements of practical building management without sacrificing efficiency.
An integrated framework that combines LLM fine-tuning using federated adaptive local low-rank adaptation (FedALoRA) with forecasting-driven autoscaling policies is proposed, making it well-suited for federated-LLM Autoscaling in dynamic edge environments.
Power grids face growing challenges from extreme weather, aging infrastructure, and increasing penetration of electric vehicles and distributed energy resources, making incident response more complex. Maintenance tickets provide valuable records, but their unstructured text requires manual interpretation, which is labo...
The exponential growth of Internet of Things (IoT) ecosystems generates massive heterogeneous big data, requiring efficient, privacy-preserving analytics that operate under strict edge resource constraints. Current federated learning (FL) frameworks often struggle with high computational costs, poor bandwidth, and inef...
Energy-Aware Adaptive Quantization and Freezing (EA-AQF), a unified framework that co-optimizes communication and computation, is presented, a unified framework that co-optimizes communication and computation and maintains robust convergence in highly heterogeneous tasks.
This work trains a CNN-LSTM forecasting model under three FL aggregation strategies under three DP-FL aggregation strategies, and advances per-sample gradient clipping in differentially private stochastic gradient descent (DP-SGD), which constrains heterogeneous client updates as the most plausible mechanism.
With the rapid development of industrial IoT and intelligent scheduling systems, real-time fusion of multi-source heterogeneous data and decision optimization have become critical challenges. Traditional centralized data collection methods face privacy leakage and communication bottlenecks, while federated learning (FL...
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