ParaBlock: Communication-Computation Parallel Block Coordinate Federated Learning for Large Language Models
arXiv:2511.19959v2 Announce Type: replace Abstract: Federated learning (FL) has been extensively studied as a privacy-preserving training paradigm. Recently, federated block coordinate descent scheme has become a popular option in training large-scale models, as it allows clients to train only a subset of the model locally instead of the entire model. However, in the era of large language models (LLMs), even a single block can contain a significant number of parameters, posing substantial communication latency, particularly for resource-constrained clients. To address this challenge in federat
The proliferation of LLMs and increasing demands for privacy-preserving AI training necessitate new paradigms like Federated Learning to address communication and computation challenges on resource-constrained devices.
This research addresses a critical bottleneck in deploying LLMs in privacy-sensitive and decentralized environments, enabling wider adoption and new applications for AI.
The development of communication-computation parallel block coordinate federated learning makes it more feasible to train large language models on distributed and less powerful client devices.
- · Edge AI providers
- · Privacy-focused AI companies
- · Federated Learning platforms
- · LLM developers
- · Centralized cloud AI services (relative decline)
- · AI models requiring massive, constant data transfer
More efficient and privacy-preserving training of LLMs becomes possible on decentralized networks.
This could accelerate the deployment of personalized and context-aware LLMs on edge devices, expanding AI's reach.
Increased decentralization of LLM training might reduce dependence on hyperscale cloud providers for certain AI applications.
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