Synergies between Federated Foundation Models and Smart Power Grids

• URL: http://arxiv.org/abs/2509.16496v1
• Date: Sat, 20 Sep 2025 02:00:07 GMT
• Title: Synergies between Federated Foundation Models and Smart Power Grids
• Authors: Seyyedali Hosseinalipour, Shimiao Li, Adedoyin Inaolaji, Filippo Malandra, Luis Herrera, Nicholas Mastronarde,
• Abstract summary: M3T Federated Foundation Models (FedFMs) enable scalable, privacy-preserving model training/fine-tuning across distributed data sources.<n>In this paper, we take one of the first steps toward introducing these models to the power systems research community.
• Score: 8.179321682277818
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The recent emergence of large language models (LLMs) such as GPT-3 has marked a significant paradigm shift in machine learning. Trained on massive corpora of data, these models demonstrate remarkable capabilities in language understanding, generation, summarization, and reasoning, transforming how intelligent systems process and interact with human language. Although LLMs may still seem like a recent breakthrough, the field is already witnessing the rise of a new and more general category: multi-modal, multi-task foundation models (M3T FMs). These models go beyond language and can process heterogeneous data types/modalities, such as time-series measurements, audio, imagery, tabular records, and unstructured logs, while supporting a broad range of downstream tasks spanning forecasting, classification, control, and retrieval. When combined with federated learning (FL), they give rise to M3T Federated Foundation Models (FedFMs): a highly recent and largely unexplored class of models that enable scalable, privacy-preserving model training/fine-tuning across distributed data sources. In this paper, we take one of the first steps toward introducing these models to the power systems research community by offering a bidirectional perspective: (i) M3T FedFMs for smart grids and (ii) smart grids for FedFMs. In the former, we explore how M3T FedFMs can enhance key grid functions, such as load/demand forecasting and fault detection, by learning from distributed, heterogeneous data available at the grid edge in a privacy-preserving manner. In the latter, we investigate how the constraints and structure of smart grids, spanning energy, communication, and regulatory dimensions, shape the design, training, and deployment of M3T FedFMs.

Related papers

• UniG2U-Bench: Do Unified Models Advance Multimodal Understanding? [50.92401586025528]
  Unified multimodal models have recently demonstrated strong generative capabilities, yet whether and when generation improves understanding remains unclear.<n>We introduce UniG2U-Bench, a comprehensive benchmark categorizing generation-to-understanding (G2U) evaluation into 7 regimes and 30 subtasks.
  arXiv  Detail & Related papers  (2026-03-03T18:36:16Z)
• Hierarchical Federated Foundation Models over Wireless Networks for Multi-Modal Multi-Task Intelligence: Integration of Edge Learning with D2D/P2P-Enabled Fog Learning Architectures [58.72593025539547]
  In this paper, we unveil an unexplored variation of M3T FFMs by proposing hierarchical federated foundation models (HF-FMs)<n>HF-FMs strategically align the modular structure of M3T FMs, comprising modality encoders, prompts, mixture-of-experts (MoEs), adapters, and task heads.<n>To demonstrate their potential, we prototype HF-FMs in a wireless network setting and release the open-source code for the development of HF-FMs.
  arXiv  Detail & Related papers  (2025-09-03T20:23:19Z)
• Seeing Further on the Shoulders of Giants: Knowledge Inheritance for Vision Foundation Models [54.517276878748305]
  Vision foundation models (VFMs) are predominantly developed using data-centric methods.<n>Many open-source vision models have been pretrained on domain-specific data.<n>We present a new model-driven approach for training VFMs through joint knowledge transfer and preservation.
  arXiv  Detail & Related papers  (2025-08-20T13:30:23Z)
• A Foundational Multi-Modal Model for Few-Shot Learning [4.57727355942957]
  Few-shot learning (FSL) aims to generalize models from a small number of labeled examples, typically fewer than 10 per class.<n>We present an innovative approach to FSL by demonstrating that a Large Multi-Modal Model (LMMM) can substantially improve the generalization of FSL models.<n>Our dataset and framework offer a unified, scalable solution that significantly lowers the barrier to applying LMMMs in data-scarce scientific domains.
  arXiv  Detail & Related papers  (2025-08-06T06:12:13Z)
• Unifying Multimodal Large Language Model Capabilities and Modalities via Model Merging [103.98582374569789]
  Model merging aims to combine multiple expert models into a single model, thereby reducing storage and serving costs.<n>Previous studies have primarily focused on merging visual classification models or Large Language Models (LLMs) for code and math tasks.<n>We introduce the model merging benchmark for MLLMs, which includes multiple tasks such as VQA, Geometry, Chart, OCR, and Grounding, providing both LoRA and full fine-tuning models.
  arXiv  Detail & Related papers  (2025-05-26T12:23:14Z)
• Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling [191.7830199016589]
  We introduce InternVL 2.5, an advanced multimodal large language model (MLLM) series that builds upon InternVL 2.0.<n>InternVL 2.5 exhibits competitive performance, rivaling leading commercial models such as GPT-4o and Claude-3.5-Sonnet.<n>We hope this model contributes to the open-source community by setting new standards for developing and applying multimodal AI systems.
  arXiv  Detail & Related papers  (2024-12-06T18:57:08Z)
• Specialized Foundation Models Struggle to Beat Supervised Baselines [60.23386520331143]
  We look at three modalities -- genomics, satellite imaging, and time series -- with multiple recent FMs and compare them to a standard supervised learning workflow.<n>We find that it is consistently possible to train simple supervised models that match or even outperform the latest foundation models.
  arXiv  Detail & Related papers  (2024-11-05T04:10:59Z)
• Synergizing Foundation Models and Federated Learning: A Survey [23.416321895575507]
  This paper discusses the potentials and challenges of synergizing Federated Learning (FL) and Foundation Models (FM) FL is a collaborative learning paradigm that breaks the barrier of data availability from different participants. It provides a promising solution to customize and adapt FMs to a wide range of domain-specific tasks using distributed datasets whilst preserving privacy.
  arXiv  Detail & Related papers  (2024-06-18T17:58:09Z)
• Forging Vision Foundation Models for Autonomous Driving: Challenges, Methodologies, and Opportunities [59.02391344178202]
  Vision foundation models (VFMs) serve as potent building blocks for a wide range of AI applications. The scarcity of comprehensive training data, the need for multi-sensor integration, and the diverse task-specific architectures pose significant obstacles to the development of VFMs. This paper delves into the critical challenge of forging VFMs tailored specifically for autonomous driving, while also outlining future directions.
  arXiv  Detail & Related papers  (2024-01-16T01:57:24Z)
• FedMS: Federated Learning with Mixture of Sparsely Activated Foundations Models [11.362085734837217]
  We propose a novel two-stage federated learning algorithm called FedMS. A global expert is trained in the first stage and a local expert is trained in the second stage to provide better personalization. We employ extensive experiments to verify the effectiveness of FedMS, results show that FedMS outperforms other SOTA baselines by up to 55.25% in default settings.
  arXiv  Detail & Related papers  (2023-12-26T07:40:26Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.