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| Hauptverfasser: | , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| Online-Zugang: | https://arxiv.org/abs/2509.01957 |
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| _version_ | 1866914038989979648 |
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| author | Chen, Evan Hosseinalipour, Seyyedali Brinton, Christopher G. Love, David J. |
| author_facet | Chen, Evan Hosseinalipour, Seyyedali Brinton, Christopher G. Love, David J. |
| contents | Foundation models (FMs) have shown remarkable capabilities in generalized intelligence, multimodal understanding, and adaptive learning across a wide range of domains. However, their deployment in harsh or austere environments -- characterized by intermittent connectivity, limited computation, noisy data, and dynamically changing network topologies -- remains an open challenge. Existing distributed learning methods such as federated learning (FL) struggle to adapt in such settings due to their reliance on stable infrastructure, synchronized updates, and resource-intensive training. In this work, we explore the potential of Federated Foundation Models (FFMs) as a promising paradigm to address these limitations. By integrating the scalability and generalization power of FMs with novel decentralized, communication-aware FL frameworks, we aim to enable robust, energy-efficient, and adaptive intelligence in extreme and adversarial conditions. We present a detailed breakdown of system-level constraints in harsh environments, and discuss the open research challenges in communication design, model robustness, and energy-efficient personalization for these unique settings. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_01957 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Federated Foundation Models in Harsh Wireless Environments: Prospects, Challenges, and Future Directions Chen, Evan Hosseinalipour, Seyyedali Brinton, Christopher G. Love, David J. Networking and Internet Architecture Foundation models (FMs) have shown remarkable capabilities in generalized intelligence, multimodal understanding, and adaptive learning across a wide range of domains. However, their deployment in harsh or austere environments -- characterized by intermittent connectivity, limited computation, noisy data, and dynamically changing network topologies -- remains an open challenge. Existing distributed learning methods such as federated learning (FL) struggle to adapt in such settings due to their reliance on stable infrastructure, synchronized updates, and resource-intensive training. In this work, we explore the potential of Federated Foundation Models (FFMs) as a promising paradigm to address these limitations. By integrating the scalability and generalization power of FMs with novel decentralized, communication-aware FL frameworks, we aim to enable robust, energy-efficient, and adaptive intelligence in extreme and adversarial conditions. We present a detailed breakdown of system-level constraints in harsh environments, and discuss the open research challenges in communication design, model robustness, and energy-efficient personalization for these unique settings. |
| title | Federated Foundation Models in Harsh Wireless Environments: Prospects, Challenges, and Future Directions |
| topic | Networking and Internet Architecture |
| url | https://arxiv.org/abs/2509.01957 |