mlsanityData to 14 Oct 2026

Decentralized Federated Learning with Model Caching on Mobile Agents

arXiv 2408.14001 ↗First posted 26 Aug 2024cs.LG cs.DCcited by 4

On the map: LLM agents and tool use → Agentic AI for wireless networks

Abstract (arXiv)

Federated Learning (FL) trains a shared model using data and computation power on distributed agents coordinated by a central server. Decentralized FL (DFL) utilizes local model exchange and aggregation between agents to reduce the communication and computation overheads on the central server. However, when agents are mobile, the communication opportunity between agents can be sporadic, largely hindering the convergence and accuracy of DFL. In this paper, we propose Cached Decentralized Federated Learning (Cached-DFL) to investigate delay-tolerant model spreading and aggregation enabled by model caching on mobile agents. Each agent stores not only its own model, but also models of agents encountered in the recent past. When two agents meet, they exchange their own models as well as the cached models. Local model aggregation utilizes all models stored in the cache. We theoretically analyze the convergence of Cached-DFL, explicitly taking into account the model staleness introduced by caching. We design and compare different model caching algorithms for different DFL and mobility scenarios. We conduct detailed case studies in a vehicular network to systematically investigate the interplay between agent mobility, cache staleness, and model convergence. In our experiments, Cached-DFL converges quickly, and significantly outperforms DFL without caching.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Placed in Agentic AI for wireless networks, one of 16 directions in LLM agents and tool use. Builds on 13 of its 25 references that are in the map. First posted on arXiv 26 Aug 2024.

Builds on

13 of 25 references.

References · 13

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  3. 2017Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithmscited by 4,349
  4. 2019Lookahead Optimizer: k steps forward, 1 step backcited by 336
  5. 2019Asynchronous Federated Optimizationcited by 267
  6. 2021Achieving Linear Speedup with Partial Worker Participation in Non-IID Federated Learningcited by 193
  7. 2022Decentralized Federated Learning: Fundamentals, State of the Art, Frameworks, Trends, and Challengescited by 138
  8. 2019Federated Learning for Wireless Communications: Motivation, Opportunities and Challengescited by 129
  9. 2021Decentralized Federated Averagingcited by 114
  10. 2023Decentralized Federated Learning: A Survey and Perspectivecited by 86
  11. 2021From Distributed Machine Learning to Federated Learning: A Surveycited by 66
  12. 2022Boost Decentralized Federated Learning in Vehicular Networks by Diversifying Data Sourcescited by 5
1 more
  1. 2023DePRL: Achieving Linear Convergence Speedup in Personalized Decentralized Learning with Shared Representationscited by 5

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 3; 2026: 1.

4 papers.

  1. 2025From Centralized to Decentralized Federated Learning: Theoretical Insights, Privacy Preservation, and Robustness Challengescited by 4
  2. 2025ADF-LoRA: Alternating Low-Rank Aggregation for Decentralized Federated Fine-Tuningcited by 4
  3. 2025Mobility-Assisted Decentralized Federated Learning: Convergence Analysis and A Data-Driven Approachcited by 0
  4. 2026Stabilizing Decentralized Federated Fine-Tuning via Topology-Aware Alternating LoRAcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026Tram-FL: Reducing Communication and Computation Costs through Sequential Model Circulation in Decentralized Federated Learning
  2. 2025FedMABench: Benchmarking Mobile Agents on Decentralized Heterogeneous User Data
  3. 2022On Decentralizing Federated Reinforcement Learning in Multi-Robot Scenarios
  4. 2024SemiDFL: A Semi-Supervised Paradigm for Decentralized Federated Learning
  5. 2025Decentralized Federated Averaging via Random Walk
  6. 2025Towards a Larger Model via One-Shot Federated Learning on Heterogeneous Client Models
  7. 2026A Secure and Private Distributed Bayesian Federated Learning Design
  8. 2025Collaborative Value Function Estimation Under Model Mismatch: A Federated Temporal Difference Analysis