FedMobileAgent: Training Mobile Agents Using Decentralized Self-Sourced Data from Diverse Users

• URL: http://arxiv.org/abs/2502.02982v1
• Date: Wed, 05 Feb 2025 08:26:17 GMT
• Title: FedMobileAgent: Training Mobile Agents Using Decentralized Self-Sourced Data from Diverse Users
• Authors: Wenhao Wang, Zijie Yu, William Liu, Rui Ye, Tian Jin, Siheng Chen, Yanfeng Wang,
• Abstract summary: We propose FedMobileAgent, a framework that trains mobile agents using self-sourced data from diverse users. In distributed settings, FedMobileAgent achieves performance comparable to centralized human-annotated models.
• Score: 50.780622043840076
• License:
• Abstract: The advancement of mobile agents has opened new opportunities for automating tasks on mobile devices. Training these agents requires large-scale high-quality data, which is costly using human labor. Given the vast number of mobile phone users worldwide, if automated data collection from them is feasible, the resulting data volume and the subsequently trained mobile agents could reach unprecedented levels. Nevertheless, two major challenges arise: (1) extracting high-level and low-level user instructions without involving human and (2) utilizing distributed data from diverse users while preserving privacy. To tackle these challenges, we propose FedMobileAgent, a collaborative framework that trains mobile agents using self-sourced data from diverse users. Specifically, it includes two techniques. First, we propose Auto-Annotation, which enables the automatic collection of high-quality datasets during users' routine phone usage with minimal cost. Second, we introduce adapted aggregation to improve federated training of mobile agents on non-IID user data, by incorporating both episode- and step-level distributions. In distributed settings, FedMobileAgent achieves performance comparable to centralized human-annotated models at less than 0.02\% of the cost, highlighting its potential for real-world applications.

Related papers

• Mobile-Agent-E: Self-Evolving Mobile Assistant for Complex Tasks [85.48034185086169]
  Mobile-Agent-E is a hierarchical multi-agent framework capable of self-evolution through past experience. Mobile-Agent-E achieves a 22% absolute improvement over previous state-of-the-art approaches.
  arXiv  Detail & Related papers  (2025-01-20T20:35:46Z)
• A3: Android Agent Arena for Mobile GUI Agents [46.73085454978007]
  Mobile GUI agents are designed to autonomously perform tasks on mobile devices. Android Agent Arena (A3) is a novel evaluation platform for assessing performance on real-world, in-the-wild tasks. A3 includes 21 widely used general third-party apps and 201 tasks representative of common user scenarios.
  arXiv  Detail & Related papers  (2025-01-02T09:03:56Z)
• Foundations and Recent Trends in Multimodal Mobile Agents: A Survey [57.677161006710065]
  Mobile agents are essential for automating tasks in complex and dynamic mobile environments. Recent advancements enhance real-time adaptability and multimodal interaction. We categorize these advancements into two main approaches: prompt-based methods and training-based methods.
  arXiv  Detail & Related papers  (2024-11-04T11:50:58Z)
• MobileExperts: A Dynamic Tool-Enabled Agent Team in Mobile Devices [17.702068044185086]
  This paper introduces MobileExperts, which for the first time introduces tool formulation and multi-agent collaboration. We develop a dual-layer planning mechanism to establish coordinate collaboration among experts. Experimental results demonstrate that MobileExperts performs better on all intelligence levels and achieves 22% reduction in reasoning costs.
  arXiv  Detail & Related papers  (2024-07-04T13:12:19Z)
• Mobile-Agent-v2: Mobile Device Operation Assistant with Effective Navigation via Multi-Agent Collaboration [52.25473993987409]
  We propose Mobile-Agent-v2, a multi-agent architecture for mobile device operation assistance. The architecture comprises three agents: planning agent, decision agent, and reflection agent. We show that Mobile-Agent-v2 achieves over a 30% improvement in task completion compared to the single-agent architecture.
  arXiv  Detail & Related papers  (2024-06-03T05:50:00Z)
• Mobile-Agent: Autonomous Multi-Modal Mobile Device Agent with Visual Perception [52.5831204440714]
  We introduce Mobile-Agent, an autonomous multi-modal mobile device agent. Mobile-Agent first leverages visual perception tools to accurately identify and locate both the visual and textual elements within the app's front-end interface. It then autonomously plans and decomposes the complex operation task, and navigates the mobile Apps through operations step by step.
  arXiv  Detail & Related papers  (2024-01-29T13:46:37Z)
• MobileAgent: enhancing mobile control via human-machine interaction and SOP integration [0.0]
  Large Language Models (LLMs) are now capable of automating mobile device operations for users. Privacy concerns related to personalized user data arise during mobile operations, requiring user confirmation. We have designed interactive tasks between agents and humans to identify sensitive information and align with personalized user needs. Our approach is evaluated on the new device control benchmark AitW, which encompasses 30K unique instructions across multi-step tasks.
  arXiv  Detail & Related papers  (2024-01-04T03:44:42Z)
• Unsupervised Domain Adaptive Learning via Synthetic Data for Person Re-identification [101.1886788396803]
  Person re-identification (re-ID) has gained more and more attention due to its widespread applications in video surveillance. Unfortunately, the mainstream deep learning methods still need a large quantity of labeled data to train models. In this paper, we develop a data collector to automatically generate synthetic re-ID samples in a computer game, and construct a data labeler to simultaneously annotate them.
  arXiv  Detail & Related papers  (2021-09-12T15:51:41Z)
• MDLdroid: a ChainSGD-reduce Approach to Mobile Deep Learning for Personal Mobile Sensing [14.574274428615666]
  Running deep learning on devices offers several advantages including data privacy preservation and low-latency response for both model robustness and update. Personal mobile sensing applications are mostly user-specific and highly affected by environment. We present MDLdroid, a novel decentralized mobile deep learning framework to enable resource-aware on-device collaborative learning.
  arXiv  Detail & Related papers  (2020-02-07T16:55:21Z)

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.