TLDR: CollaPipe is a new distributed learning framework that combines pipeline parallelism and federated learning to efficiently train large language models (LLMs) on mobile devices and edge servers. It adaptively splits LLM encoders across devices and uses optimization algorithms to manage resources like bandwidth and power, significantly reducing training latency, improving computational efficiency, and lowering memory usage in heterogeneous mobile edge networks.
The demand for intelligent mobile applications is rapidly increasing, making the training of large language models (LLMs) crucial for mobile edge computing (MEC) networks. However, training these complex models in such environments presents significant challenges, including heavy computational requirements, high end-to-end latency, and difficulties in achieving broad model generalization. Addressing these issues, a new framework called CollaPipe has been introduced.
CollaPipe is a hybrid distributed learning framework that cleverly combines collaborative pipeline parallelism with federated aggregation. This integration aims to support the development of self-evolving intelligent networks. At its core, CollaPipe adaptively partitions the encoder part of an LLM into variable-sized segments, deploying them across various mobile devices for pipeline-parallel training. Meanwhile, the decoder component is hosted on edge servers, where it handles generative tasks. After local training, a global model update is performed through federated aggregation, ensuring privacy and collaborative learning.
To boost training efficiency, CollaPipe formulates a sophisticated optimization problem. This problem adaptively allocates model segments, micro-batches, network bandwidth, and transmission power. The researchers derived a closed-form convergence bound, which was then used to design a Dynamic Segment Scheduling and Resource Allocation (DSSDA) algorithm. This algorithm, based on Lyapunov optimization, ensures the system remains stable even under long-term constraints.
Extensive experiments were conducted using both Transformer and BERT models on various downstream tasks, including machine translation, named entity recognition, and sentence classification. The results were impressive: CollaPipe improved computation efficiency by up to 15.09%, reduced end-to-end latency by at least 48.98%, and cut single device memory usage by more than half. These achievements demonstrate its capability to enable online learning in diverse and dynamic communication environments.
How CollaPipe Works in Detail
The framework operates within a two-tier hierarchical network architecture, consisting of an edge server and multiple clusters. Each cluster contains several devices and a designated Control Unit (CU) that manages data and coordination. The LLM is modularized: the embedding module is on CUs, the decoder on the edge server, and the computationally intensive encoder is split into segments for adaptive deployment across devices within each cluster. These modules are connected sequentially via wireless links, facilitating efficient data flow.
CollaPipe organizes learning into two levels:
- Device-to-Device (D2D) Collaboration: Within each cluster, devices communicate directly to collaboratively execute pipeline-parallel learning. They exchange intermediate activations, labels, and gradients, enabling efficient distributed training.
- Device-to-Edge (D2E) Collaboration: CUs from different clusters transmit local encoder parameters to the base station (BS) for federated learning. The BS then trains the decoder module and performs federated aggregation to update the global LLM parameters.
The learning process involves several steps: determining key hyperparameters like the number of micro-batches, scheduling segments to devices based on their capabilities, performing forward and backward propagation of the LLM encoder and decoder, and finally, global model aggregation and updating.
Addressing Network Challenges
The paper also delves into the communication model, considering both D2E and D2D interactions. It accounts for uplink rates, transmission delays, and energy consumption, including interference in wireless environments. A pipeline parallelism model is designed to manage computation and communication overhead, ensuring consistent delays across devices despite their heterogeneous capabilities.
The convergence analysis of CollaPipe highlights how factors like the number of model segments, micro-batch size, and communication interference impact model divergence. This analysis guided the formulation of a stochastic optimization problem aimed at minimizing average training delay while adhering to constraints on energy consumption, memory usage, and network resources. The DSSRA algorithm, leveraging Lyapunov optimization, effectively decouples this complex problem into manageable per-round sub-problems, ensuring long-term system stability.
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Experimental Validation and Impact
The experiments showed that CollaPipe consistently achieved lower computational latency compared to baseline methods like VanillaFL, PipeLine, and TITANIC. For instance, it reduced training delay by 18.94% compared to TITANIC and 15.09% compared to VanillaFL in certain scenarios. The framework also offers greater flexibility in memory usage, dynamically adjusting based on the number of encoder blocks assigned to each device, making it ideal for resource-constrained edge environments. Furthermore, by centralizing training data in the CU, participating devices only contribute computational resources, reducing data-sharing concerns and device management overhead.
In conclusion, CollaPipe represents a significant advancement in collaborative LLM training within heterogeneous edge networks. By integrating pipeline parallelism and federated aggregation with adaptive scheduling and resource allocation, it offers a robust solution for efficient and stable distributed AI. For more details, you can refer to the full research paper: CollaPipe: Adaptive Segment-Optimized Pipeline Parallelism for Collaborative LLM Training in Heterogeneous Edge Networks.
