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TALLOP: Joint Optimization of Local Training and Communication Compression for Time-Constrained Federated Learning

Honoka Anada, Yafei Ou, Shinya Takamaeda-Yamazaki

arXiv:2610.22996v1cs.LGcs.DC

TL;DR

Heterogeneous clients and communication-limited rounds make it challenging to reach target accuracy quickly under fixed time budgets. TALLOP predicts training and compression losses to choose a joint allocation, and generally matches or improves time-to-accuracy against training-only optimization in evaluated settings, though gains can be modest or absent.

  • Problem

    Under heterogeneous, communication-limited conditions, choosing a training-communication allocation within a fixed round budget is challenging because outcomes depend on training progress and compression degradation.

    Source: §1 · p. 1, §3.2 · p. 4

  • Method

    TALLOP combines LENS predictions of future local-training loss with CLEN estimates of compression loss to select a per-round training and communication allocation.

    Source: §1 · p. 2, §3.2.3 · p. 6

  • Results

    In evaluated settings, TALLOP generally matched or improved time-to-accuracy relative to training-only optimization, but improvements were modest or absent in some settings.

    Source: §4.1 · p. 8, §5 · p. 9

  • Takeaways & Limitations

    Joint workload and compression optimization is competitive in the evaluated settings, while its additional benefit depends on the setting and can be limited when computation dominates.

    Source: §4.1 · p. 8, §5 · p. 9

    The experiments used computation and communication models rather than real deployments and a single random seed without error bars.

    Source: §4.1 · p. 8

Abstract

from arXiv · show

Minimizing the wall-clock time required to reach a target accuracy is crucial in federated learning (FL), where many applications require frequent model updates as data distributions evolve over time. Nevertheless, in heterogeneous environments, slow clients with limited computational and communication capabilities can delay global synchronization, forcing faster clients to wait and increasing the overall training time. To mitigate this straggler effect, recent studies have introduced per-round time constraints and adapted client workloads within the available time. However, these approaches primarily focus on local computation, whereas communication can become a major bottleneck in communication-limited environments, motivating the joint optimization of training workload and communication compression. Although previous studies have explored such joint optimization, determining an effective training--communication balance under a fixed per-round time budget remains challenging, as the resulting model performance depends on both training progress and compression-induced degradation. We therefore propose TALLOP, which jointly optimizes the local training workload and communication compression under per-round time constraints. TALLOP predicts the training loss achievable under different computation budgets from the early-stage mini-batch loss trajectory (LENS), and estimates the degradation caused by communication compression using a compression-error model (CLEN). TALLOP combines these predictions to select the training-communication allocation that minimizes the post-compression loss under the given round time constraint. Experiments in heterogeneous FL environments show that TALLOP generally achieves competitive or improved time-to-accuracy compared with approaches that optimize only local training or neither, although the benefit over training-only adaptation depends on the setting.

1. Introduction

Heterogeneous clients and communication-limited rounds make time-to-accuracy depend on balancing local training against communication. TALLOP predicts post-compression loss to select that allocation, and experiments show generally competitive or improved time-to-accuracy.

1. Introduction

  • Slow clients can delay synchronous rounds while faster clients wait, increasing the wall-clock time to target accuracy.Differences in computation and communication bandwidth create variation in local training and transmission time.Source: §1 · p. 1
  • Deadline-aware methods mainly adapt local training, but communication can take as long as or longer than training in communication-limited environments.Under a fixed round budget, more training time can mean stronger compression and greater information loss, while loss-free communication can leave insufficient training time.Source: §1 · pp. 1–2
  • Prediction-based joint optimization under fixed per-round deadlines remains insufficiently explored, especially in heterogeneous, communication-limited environments.Existing approaches focus mainly on computation or do not directly optimize the training-communication allocation using predicted loss.Source: §1 · p. 2
  • TALLOP jointly selects each client’s local training workload and communication compression ratio under a per-round time constraint.LENS extrapolates mini-batch loss trajectories to predict future training loss, while CLEN estimates compression-induced loss.Source: §1 · p. 2
  • TALLOP generally provides competitive or improved time-to-accuracy compared with optimizing only local training or neither, with overhead equivalent to training on a few samples.The benefit over training-only adaptation depends on the setting.Source: §1 · p. 2

2. Related Work

Prior work addresses stragglers through asynchronous aggregation or client-specific local workloads, and some methods jointly optimize training and communication. TALLOP’s related-work gap is direct loss-based allocation under fixed deadlines.

2.1. Time-to-Accuracy Optimization under Device Heterogeneity in FL

  • Asynchronous FL removes synchronization barriers but can introduce stale updates and is incompatible with mechanisms such as secure aggregation.Deadline-aware methods generally focus on computation and do not explicitly optimize communication time.Source: §2.1 · pp. 2–3
  • Synchronous methods adapt local epochs, batch sizes, or updated parameters to match clients’ computational capabilities and reduce idle waiting.Deadline-aware approaches also adapt local workloads so clients can complete computation and upload within a round’s available time.Source: §2.1 · p. 2
  • Communication-limited rounds couple training workload and compression: stronger compression can shorten transmission but may increase information loss.The coupling matters when model transmission occupies a substantial share of the round budget.Source: §2.1 · p. 3

2.2. Joint Optimization of Training and Communication in FL

  • Prior joint approaches include synchronization-interval, compression-ratio, quantization, and sparsity optimization; FedLamp uses convergence bounds for update frequencies and compression ratios.Convergence-bound optimization does not directly estimate learning progress for different allocations within a fixed round deadline.Source: §2.2 · p. 3

3.1. Problem Formulation

The formulation models synchronous client training and compressed uploads within a fixed round budget. It recasts joint workload and transmission optimization as a time-allocation problem, with explicit modeling assumptions and scope choices.

3.1.1. System Model

  • Each round has a time budget, selected clients train locally, and only updates arriving before the deadline are included in synchronous aggregation.The model assumes downloads are uncompressed and optimizes local training and upload compression, not download time.Source: §3.1.1 · p. 3
  • Clients compress parameter differences by transmitting only the top p fraction of elements by absolute value; this study focuses on unstructured sparsification.Other compression methods are outside scope, and transmitted parameter elements are assumed to use 32 bits.Source: §3.1.1 · p. 3

3.1.2. Optimization Objective

  • The objective is to choose local training steps s and transmission ratio p jointly to minimize transmitted-model loss subject to the round-time constraint.Loss is evaluated on the client’s local dataset.Source: §3.1.2 · p. 3

3.1.3. Time Allocation Parameter

  • The scalar ρ represents the fraction of post-download round time allocated to local training, reparameterizing the coupled feasible choices.The remaining time is available for model transmission.Source: §3.1.3 · p. 4
  • Each feasible ρ determines maximum local training steps s(ρ) and the maximum feasible transmission ratio p(ρ) within the remaining communication time.The client’s computation capability and communication bandwidth determine this mapping.Source: §3.1.3 · p. 4, §3.1.4 · p. 4

3.1.4. Mapping Time Allocation to Local Training and Communication

  • The timing model uses client FLOPS, upload and download bandwidths, model size, per-sample operations, and batch size; it assumes training time scales with operations.Other bottlenecks, including memory bandwidth, are ignored.Source: §3.1.4 · p. 4
  • Download time is subtracted from the round budget first; remaining time is divided between training and upload, with upload time determined by the transmission ratio.For p < 1, updates use sparse-vector values and indices; p = 1 uses full-model transmission.Source: §3.1.4 · p. 4

3.2. TALLOP Design

TALLOP predicts post-compression loss across feasible training–communication allocations and selects the allocation with the lowest predicted loss. It combines LENS, which extrapolates future training loss, with CLEN, which estimates compression-induced loss.

3.2. TALLOP Design

  • TALLOP combines predicted training and compression losses to choose the time allocation factor that minimizes post-compression loss under the round deadline.Each allocation determines a local training workload and a transmission ratio.Source: §3.2 · p. 4, Figure 2 · p. 5

3.2.1. LENS: Loss-Curve Extrapolation via Smoothing.

  • LENS smooths noisy mini-batch losses with a monotonically decreasing loss-curve model and extrapolates loss at future training steps.The curve parameters are fitted to observed mini-batch losses.Source: §3.2.1 · p. 5
  • LENS’s smooth, monotonically decreasing fit may not hold for short or noisy local training sequences.When fewer than three observations are available or fitting fails to converge, the initial parameters are used as a fallback.Source: §3.2.1 · p. 5

3.2.2. CLEN: Compressed-model Loss Estimation by Error Norm.

  • CLEN estimates post-compression loss from the training loss and a compression-error model based on parameter perturbation.Its loss-increase approximation uses a proportionality constant.Source: §3.2.2 · pp. 5–6
  • CLEN’s assumptions include near-stationarity and compression errors without strong directional bias, and its accuracy can depend on architecture, layer, and training stage.The near-stationarity assumption may not hold early in training.Source: §3.2.2 · p. 5
  • Predicted and measured compressed-model losses closely matched across MobileNetV2 and ViT, training stages, and non-zero ratios.A slight overestimate occurred under the Epoch 0 setting; Figure 3 compares predicted and actual losses across ratios.Source: §3.2.2 · p. 6, Figure 3 · p. 6

3.2.3. Overall Algorithm of TALLOP.

  • TALLOP uses ternary search to select the allocation, assuming predicted loss is unimodal, and performs prediction once per communication round.For practical estimation, it substitutes parameters at the estimation step for unavailable future parameters.Source: §3.2.3 · pp. 6–7

4. Experiments and Discussion

Experiments evaluate TALLOP’s time-to-accuracy performance and computational overhead across heterogeneous FL settings. The results show generally favorable time-to-accuracy, with benefits that vary by system configuration and a small estimated overhead.

4.1. Time-to-Accuracy Evaluation

  • The evaluation covers three model-dataset combinations across 100 non-IID clients, with 20 clients selected per round and two system configurations.The experiments use simulated computation and communication performance models rather than physical deployments; the reported results use one random seed without error bars.Source: §4.1 · pp. 7–8
  • Deadline-based adaptation generally improves FA and TTA over vanilla FL, and TALLOP beats training-only optimization in TTA at most targets.Introducing deadlines reduces TTA by half or more at several targets; joint optimization’s added benefit is small in several settings, particularly when computation dominates.Source: §4.1 · p. 8
  • TALLOP achieves the shortest or tied-shortest TTA at most targets, while its FA advantage is stronger under Configuration 2 than Configuration 1.Table 2 reports FA and TTA across evaluated dataset/model combinations; Figure 4 plots MobileNetV2/CIFAR-10 accuracy over wall-clock time.Source: §4.1 · p. 8, Table 2 · p. 9

4.2. Computational Overhead Analysis of TALLOP

  • TALLOP’s additional per-round computation is estimated at O(P log P log(1/ε)), corresponding to training on at most a few MobileNetV2 samples.The estimate assumes approximately 2 × 10^6 parameters and log(1/ε) ≈ 10; the analysis counts O(log(1/ε)) compressed-loss evaluations, each dominated by O(P log P) work.Source: §4.2 · p. 9

5. Conclusion

TALLOP jointly allocates local training and communication compression under a predefined per-round time budget. In evaluated settings, it generally matched or improved TTA, though gains were modest or absent in some settings and the baseline comparison was narrow.

5. Conclusion

  • TALLOP predicts post-compression loss across training-communication allocations to jointly optimize local workload and compression under a fixed round budget.The conclusion frames the setting as heterogeneous FL where communication time is non-negligible.Source: §5 · p. 9
  • TALLOP generally matched or improved TTA over training-only optimization and was competitive with three baselines on MobileNetV2/CIFAR-10.Improvements were modest or absent in some settings; baseline comparisons covered a single model and random seed.Source: §5 · p. 9
  • The method assumes a predefined round-time budget; future work includes jointly optimizing that budget and evaluating more dynamic deployment conditions.The paper also identifies asynchronous FL as a possible extension.Source: §5 · p. 9

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