GeFL: Model-Agnostic Federated Learning with Generative Models
Federated learning (FL) is a promising paradigm in distributed learning while preserving the privacy of users. However, the increasing size of recent models makes it unaffordable for a few users to encompass the model. It leads the users to adopt heterogeneous models based on their diverse computing capabilities and network bandwidth. Correspondingly, FL with heterogeneous models should be addressed, given that FL typically involves training a single global model. In this paper, we propose Generative Model-Aided Federated Learning (GeFL), incorporating a generative model that aggregates global knowledge across users of heterogeneous models. Our experiments on various classification tasks demonstrate notable performance improvements of GeFL compared to baselines, as well as limitations in terms of privacy and scalability. To tackle these concerns, we introduce a novel framework, GeFL-F. It trains target networks aided by feature-generative models. We empirically demonstrate the consistent performance gains of GeFL-F, while demonstrating better privacy preservation and robustness to a large number of clients. Codes are available at [1].
Code (0)
등록된 구현이 없습니다.
Tasks
Federated LearningmodelMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
EdgeFL: A Lightweight Decentralized Federated Learning Framework
Federated Learning (FL) has emerged as a promising approach for collaborative machine learning, addressing data privacy concerns. However, existing FL platforms and frameworks often present challenges for software engine…
Federated LearningEdgeFlowerTune: Evaluating Federated LLM Fine-Tuning Under Realistic Edge System Constraints
Federated fine-tuning offers a promising paradigm for adapting large language models (LLMs) on edge devices by leveraging the rich, diverse, and continuously generated data from smartphones and IoT devices without compro…
EdgeFLow: Serverless Federated Learning via Sequential Model Migration in Edge Networks
Federated Learning (FL) has emerged as a transformative distributed learning paradigm in the era of Internet of Things (IoT), reconceptualizing data processing methodologies. However, FL systems face significant communic…
Federated LearningSageflow: Robust Federated Learning against Both Stragglers and Adversaries
While federated learning (FL) allows efficient model training with local data at edge devices, among major issues still to be resolved are: slow devices known as stragglers and malicious attacks launched by adversaries. …
Federated LearningJudgeFlow: Agentic Workflow Optimization via Block Judge
Optimizing LLM-based agentic workflows is challenging for scaling AI capabilities. Current methods rely on coarse, end-to-end evaluation signals and lack fine-grained signals on where to refine, often resulting in ineffi…
Mathematical ReasoningCode Generation