ACE-Sync: An Adaptive Cloud-Edge Synchronization Framework for Communication-Efficient Large-Scale Distributed Model Training

Large-scale deep learning models impose substantial communication overh ead in distributed training, particularly in bandwidth-constrained or heterogeneous clo ud-edge environments. Conventional synchronous or fixed-compression techniques o ften struggle to balance communication cost, convergence stability, and model accura cy. To address these challenges, we propose ACE-Sync, an Adaptive Cloud-Edge Sy nchronization Framework that integrates (1) an attention-based gradient importance p redictor, (2) a differentiated parameter compression strategy, and (3) a hierarchical cl oud-edge coordination mechanism. ACE-Sync dynamically selects which parameter groups to synchronize and determines appropriate compression levels under per-devic e bandwidth budgets. A knapsack-based optimization strategy is adopted to maximize important gradient preservation while reducing redundant communication. Furthermo re, residual-based error compensation and device clustering ensure long-term converg ence and cross-device personalization. Experiments show that ACE-Sync substantiall y reduces communication overhead while maintaining competitive accuracy. Compar ed with FullSync, ACE-Sync lowers communication cost from 112.5 GB to 44.7 GB (a 60% reduction) and shortens convergence from 41 to 39 epochs. Despite aggressiv e communication reduction, ACE-Sync preserves high model quality, achieving 82. 1% Top-1 accuracy-only 0.3% below the full-synchronization baseline-demonstrating its efficiency and scalability for large-scale distributed training. These results indicate that ACE-Sync provides a scalable, communication-efficient, and accuracy-preservin g solution for large-scale cloud-edge distributed model training.

💡 Research Summary

The paper introduces ACE‑Sync, an adaptive cloud‑edge synchronization framework designed to alleviate the communication bottleneck that dominates large‑scale distributed deep‑learning training, especially when edge devices operate under heterogeneous and bandwidth‑constrained networks. ACE‑Sync consists of four tightly coupled components: (1) an attention‑based parameter‑importance predictor that fuses temporal gradient statistics (magnitude and variance) with structural cues (layer depth, density, receptive‑field relationships) to assign a score I(θi) = α·Att_temp(gi) + (1‑α)·Att_struct(θi); (2) an adaptive compression‑expansion scheduler that maps each device’s real‑time bandwidth estimate Bk(t) to a compression ratio ck(t) = c_min + (c_max‑c_min)·exp(‑β·Bk(t)), thereby increasing compression when bandwidth is scarce and relaxing it when conditions improve; (3) a hierarchical cloud‑edge synchronization protocol where edges transmit only the top‑p fraction of high‑importance parameters in full precision while low‑importance parameters are quantized‑sparsified (Q(gi) = sign(gi)·‖gi‖2·qi) and sent less frequently; the cloud aggregates updates using device‑specific weights ωk, monitors per‑device divergence Dk(t)=‖θk(t)‑θ(t)‖2, and dynamically adjusts synchronization frequency or compression levels when divergence exceeds a threshold; and (4) a residual‑based error‑feedback mechanism (gi ← gi + γ·ei) that accumulates quantization errors ei and periodically corrects them, ensuring that aggressive compression does not permanently degrade gradient information. The authors formulate the selection of parameters to transmit as a knapsack problem, guaranteeing maximal preservation of important gradients under a fixed communication budget. Experiments were conducted on a 350‑million‑parameter Transformer model trained across 64 heterogeneous edge nodes (Jetson AGX Xavier, ARM accelerators, low‑power CPUs) and 16 NVIDIA A100 GPUs in the cloud. Network conditions were varied between 5‑200 Mbps and 10‑300 ms latency to emulate realistic cloud‑edge scenarios. Compared with three baselines—FullSync (full‑precision synchronous aggregation), Top‑k sparsification, and FedAvg‑Periodic Sync—ACE‑Sync reduced total transmitted data from 112.5 GB to 44.7 GB (≈60 % reduction), shortened convergence from 41 to 39 epochs, and achieved 82.1 % Top‑1 accuracy, only 0.3 % below the FullSync baseline. In contrast, static sparsification methods suffered >1 % accuracy loss, and fixed‑interval federated averaging showed poor adaptability to bandwidth fluctuations. The results demonstrate that ACE‑Sync’s dynamic importance estimation, real‑time bandwidth‑aware compression, and hierarchical scheduling jointly provide a scalable, communication‑efficient solution without sacrificing convergence stability. The paper concludes with suggestions for future work, including asynchronous extensions, broader model‑family evaluations, and energy‑efficiency studies on real mobile hardware.