[Paper] ParaBlock: Communication-Computation Parallel Block Coordinate Federated Learning for Large Language Models

Source: arXiv - 2511.19959v1

Overview

The paper introduces ParaBlock, a new federated learning (FL) framework designed to train or fine‑tune large language models (LLMs) across many devices while keeping data private. By overlapping communication with local computation, ParaBlock cuts the “wait‑time” that normally dominates FL when each client must download and upload massive model blocks, making FL viable for resource‑constrained edge devices.

Key Contributions

• Parallel Communication‑Computation Pipeline: A two‑thread design that lets a client start sending the next model block while still processing the current one, effectively hiding network latency.
• Theoretical Guarantees: Proof that ParaBlock retains the same convergence rate as classic federated block coordinate descent (F-BCD), despite the overlapping schedule.
• Scalable to LLMs: Demonstrated on instruction‑following and mathematical‑reasoning fine‑tuning tasks with models up to several hundred million parameters.
• Empirical Speed‑up: Experiments show up to 2× reduction in wall‑clock communication time with negligible loss in downstream performance (often <0.2% BLEU/accuracy drop).
• Open‑source Prototype: The authors release a lightweight PyTorch‑based implementation that plugs into existing FL toolkits (e.g., Flower, FedML).

Methodology

1. Block Partitioning: The global LLM is split into blocks (e.g., transformer layers or groups of layers). Each FL round, a client receives only one block to update.
2. Dual‑Thread Execution:
   • Thread A (Computation): Performs local SGD on the received block using the client’s private data.
   • Thread B (Communication): Simultaneously streams the next block from the server and begins uploading the updated parameters of the previous block.
3. Synchronization: The server aggregates block updates asynchronously, then schedules the next block for each client based on a simple round‑robin policy.
4. Convergence Analysis: By modeling the overlap as bounded staleness, the authors extend standard F‑BCD proofs to show that the expected gradient norm decays at O(1/√T), identical to the non‑overlapped case.

The approach requires only modest changes to existing FL pipelines—primarily adding non‑blocking send/receive calls and a small buffer to hold the “in‑flight” block.

Results & Findings

• Wall‑clock training time dropped from ~12 h to ~7 h on a 20‑client simulation with 10 Mbps uplink/downlink.
• Network traffic remained unchanged (same amount of data transferred), confirming that the speed‑up comes purely from latency hiding.
• The method proved robust across heterogeneous client speeds; slower devices automatically spent more time in the computation thread, while faster ones kept the communication pipeline busy.

Practical Implications

• Edge‑AI Companies: Enables on‑device fine‑tuning of LLMs for personalized assistants, chatbots, or domain‑specific knowledge without exposing raw user data.
• Cost‑Effective Cloud‑Edge Collaboration: Reduces the need for high‑bandwidth links or expensive edge servers; even 4G/5G connections become sufficient for large‑scale FL.
• Developer Tooling: The open‑source prototype can be dropped into existing FL stacks, letting engineers experiment with block‑wise updates and overlapping I/O with minimal code changes.
• Regulatory Compliance: By keeping data local and shrinking the communication window, ParaBlock eases auditability for GDPR‑type constraints where data residency matters.

Overall, ParaBlock opens the door for real‑time, privacy‑preserving LLM adaptation on smartphones, IoT gateways, and other low‑resource nodes.

Limitations & Future Work

• Block Size Sensitivity: Very large blocks (e.g., >100 M parameters) still incur noticeable latency; future work could explore dynamic block resizing or gradient compression.
• Asynchronous Aggregation Overhead: While the paper shows convergence under bounded staleness, extreme heterogeneity (e.g., some clients offline for hours) may degrade performance.
• Security Considerations: Overlapping communication could expose timing side‑channels; integrating secure aggregation with ParaBlock remains an open challenge.
• Broader Benchmarks: Experiments focus on instruction‑following and math tasks; applying ParaBlock to multimodal LLMs or reinforcement‑learning‑from‑human‑feedback (RLHF) pipelines is left for later studies.

The authors suggest extending the parallelism concept to pipeline parallel FL across multiple blocks simultaneously, which could further shrink training time for next‑generation LLMs.

Authors

• Yujia Wang
• Yuanpu Cao
• Jinghui Chen

Paper Information

• arXiv ID: 2511.19959v1
• Categories: cs.LG, cs.DC
• Published: November 25, 2025
• PDF: Download PDF