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Expanding the Reach of Federated Learning by Reducing Client Resource Requirements
Sebastian Caldas, Jakub Konečny, H. Brendan McMahan, Ameet Talwalkar
TL;DR
Federated Learning communication costs can restrict model capacity and user participation on heterogeneous edge networks. The paper combines lossy compression of server-to-client models with Federated Dropout, which trains smaller sub-models and complements client-to-server compression. The combined strategies reduce communication and local computation substantially without degrading final-model accuracy, although more aggressive dropout slows convergence.
Problem
Heterogeneous edge networks and high-capacity models make FL communication demanding, risking restricted model capacity and exclusion of users with limited bandwidth.
Method
The paper applies lossy compression to server-to-client global models and uses Federated Dropout so clients train smaller sub-models whose updates map to the global model.
Results
Up to 14× lower server-to-client communication, 28× lower client-to-server communication, and 1.7× lower local computation are achieved without degrading final-model accuracy.
Takeaways & Limitations
The strategies reduce FL’s communication overhead and client resource requirements, supporting the paper’s goal of expanding FL’s reach.
Takeaways & Limitations
More aggressive Federated Dropout rates tend to slow model convergence, despite sometimes producing higher accuracy.
Abstract
from arXiv · showhide
Communication on heterogeneous edge networks is a fundamental bottleneck in Federated Learning (FL), restricting both model capacity and user participation. To address this issue, we introduce two novel strategies to reduce communication costs: (1) the use of lossy compression on the global model sent server-to-client; and (2) Federated Dropout, which allows users to efficiently train locally on smaller subsets of the global model and also provides a reduction in both client-to-server communication and local computation. We empirically show that these strategies, combined with existing compression approaches for client-to-server communication, collectively provide up to a $14\times$ reduction in server-to-client communication, a $1.7\times$ reduction in local computation, and a $28\times$ reduction in upload communication, all without degrading the quality of the final model. We thus comprehensively reduce FL's impact on client device resources, allowing higher capacity models to be trained, and a more diverse set of users to be reached.
1 Introduction
Federated Learning faces severe communication bottlenecks on heterogeneous edge networks, especially for high-capacity models, risking client exclusion and reduced model accuracy. The paper proposes lossy server-to-client compression and Federated Dropout, achieving substantial resource reductions without degrading accuracy.
- FL’s slower networks and larger worker populations increase communication bottlenecks, stragglers, and device dropouts, especially for high-capacity models.
- Large-model training can systematically exclude clients with restricted bandwidth, while low-capacity models reduce communication at the expense of accuracy.
- The paper proposes lossy compression of downloaded global models and Federated Dropout, which trains smaller sub-models while preserving updates applicable to the global model.
- 14× lower downloaded-model size, 28× lower corresponding update size, and 1.7× lower local computation are achieved without degrading accuracy, at the expense of slightly slower convergence.
2 Related Work
Related work addresses distributed-learning communication overhead through sparsification, quantization, subsampling, and client-to-server compression. However, prior approaches had not addressed server-to-client model compression, which remains burdensome for constrained users and large client populations.
- Federated Learning trains global models across devices without moving their local data, but requires continued server-client exchanges over potentially slow networks.
- Communication-efficient learning commonly sparsifies, quantizes, or randomly subsamples updates, with reported preservation of trained-model quality despite introduced noise.
- Prior FL compression work applied lossy compression to client-to-server model updates and used randomized Hadamard transforms to reduce quantization error.
- Earlier distributed-learning and FL work did not compress server-to-client exchanges, leaving downloaded global models burdensome for constrained users and potentially for servers serving many devices.
3 Methods
The methods combine lossy model compression with Federated Dropout to reduce the size of exchanged models and local training costs. Compression transforms, subsampling, and quantization reduce transmitted representations, while clients train mapped smaller sub-models whose updates return to the global model.
- Methods: The proposed methods are lossy compression and Federated Dropout, and the paper presents them as fully compatible during FL training.
- Lossy Compression: Each weight matrix is reshaped into a vector, basis-transformed, subsampled, and quantized before transmission, then inverse-transformed after receipt.
- Lossy Compression: Kashin’s representation spreads vector information across dimensions to reduce error from later perturbations such as quantization.
- Lossy Compression: Subsampling zeros a 1 −s fraction of matrix elements, rescales the remainder, and communicates nonzero values plus a random seed for index recovery.
- Federated Dropout: Federated Dropout trains updates for smaller sub-models that are subsets of the global model, allowing their updates to be mapped back to that model.
- Federated Dropout: Federated Dropout reduces upload size and local computation because clients use smaller matrices or fewer convolutional filters during gradient evaluation.
4 Experimental Results
Experiments evaluate lossy compression, Federated Dropout, and their combination with client-to-server compression across MNIST, CIFAR-10, and EMNIST. The combined strategies reduce communication and computation substantially while preserving accuracy under selected settings.
- Experimental Setup: The experiments use FedAvg on MNIST, CIFAR-10, and EMNIST, with repeated evaluations of compression and Federated Dropout settings.MNIST and CIFAR-10 use artificial IID partitions, while EMNIST uses a more realistic user-based partition.
- Lossy Compression: Compression varies the basis transform, kept-weight fraction, and quantization bits; tested transforms include identity, randomized Hadamard, and Kashin’s representation.The experiments report mean accuracy over 10 repetitions and exclude biases from compression.
- Lossy Compression: Nearly 8× lower communication is achieved by quantizing every model to 4 bits while matching or sometimes slightly outperforming the baseline accuracy.A suitable parameter setting exists for every evaluated model.
- Lossy Compression: Kashin’s representation is most useful under aggressive quantization, whereas subsampling is less helpful server-to-client than for client-to-server updates.Server-to-client global-model compression requires more conservative settings than client-to-server update compression.
- Federated Dropout: A federated dropout rate below 1.0 can match or improve final accuracy, with 0.75 working across the evaluated models.At 0.75, MNIST and EMNIST obtain approximately 43% reductions in fully connected-layer size and computation, while CIFAR-10 gains 25%.
- Combined Strategies: Combined compression and Federated Dropout provide up to 14× download savings, 28× upload savings, and 1.7× lower local computation without accuracy degradation.For CIFAR-10 specifically, the reported savings are 10× server-to-client, 21× client-to-server, and 1.3× local computation; a 0.75 dropout rate with moderate or conservative compression is suggested as a practical starting point.
5 Conclusions and Open Questions
The paper targets heterogeneous-edge FL bandwidth demands with lossy server-to-client compression and Federated Dropout. Combined with prior methods, these strategies reduce communication and computation while motivating future work on fairness and larger server-managed models.
- Heterogeneous, slow edge networks and demanding deep models risk restricting trainable model types and excluding groups of users.
- Up to 14× lower server-to-client communication, 28× lower client-to-server communication, and 1.7× lower local computation are reported without accuracy degradation.The reductions combine the paper’s strategies with previous compression methods.
- Figure 5 evaluates combined compression and Federated Dropout on CIFAR-10 and EMNIST.
- Future work includes adaptive compression and dropout for fairness, aiming to prevent unfairly biased models.
- Federated Dropout suggests aggregating smaller, potentially personalized sub-models into a larger model managed by the server.The paper states that the associated sub-model creation and aggregation overhead is justified in FL.
A Kashin’s Representation
The appendix presents Kashin’s representation from theoretical and practical perspectives, along with preliminary size–accuracy results. It focuses on the representation’s potential relative to the randomized Hadamard transform.
- Kashin’s representation is discussed theoretically in Section A.1 and practically in Section A.2.
- The appendix includes preliminary results on Kashin’s representation’s size-versus-accuracy trade-off.
- The preliminary results assess whether Kashin’s representation can dominate the random Hadamard transform on that trade-off.
A.1 Theoretical Overview
Kashin’s representation uses a tight frame satisfying an uncertainty principle to produce coefficients with minimal dynamic range. Under an assumption about the randomized Hadamard transform, this theory improves quantization-error bounds.
- Kashin’s representation converts tight-frame coefficients into a more robust representation with the smallest possible dynamic range.The construction requires a tight frame satisfying a weaker-than-RIP uncertainty principle.
- The theoretical result connects Kashin’s representation to reduced perturbation sensitivity in quantized coefficients.
- Assuming the subsampled randomized Hadamard transform satisfies the uncertainty principle, the theorem replaces a prior lemma and removes logarithmic dimension dependence from the bound.The resulting bound matches the lower bounds.
A.2 Practical Considerations
The practical procedure iteratively computes frame coefficients, projects onto an L∞ ball, and reconstructs errors. The implementation uses a randomized Hadamard frame with two iterations, while power-of-two dimensions require padding considerations.
- Each iteration computes frame coefficients, projects them onto an L∞ ball, and reconstructs the error in the original domain.The next iteration starts from the reconstructed error with a smaller ball.
- The implementation uses a randomized Hadamard transform, two iterations, δ = 1, and an exact-representation variant omitting the final projection.The dominant computation consists of three fast Walsh-Hadamard transform applications.
- An 80-dimensional vector is padded to dimension 128 before applying randomized sign diagonalization and the fast Walsh-Hadamard transform.
- Power-of-two initial dimensions require padding to the next power of two to obtain a benefit over the Hadamard transform alone.
A.3 Dominance over Hadamard
The experiment compares identity, randomized Hadamard, and Kashin representations across subsampling and quantization settings. Kashin’s representation dominates the size–accuracy trade-off in the MNIST results, while requiring parameter optimization.
- Motivation: Kashin’s representation is hypothesized to outperform randomized Hadamard on the size–accuracy trade-off because of its theoretical properties.The comparison motivates the preliminary experiment.
- Experimental setup: The experiment compresses a trained MNIST model using linear transforms, subsampling ratios, and quantization bits, then evaluates accuracy and L2 distance.The transforms include identity, randomized Hadamard, and Kashin’s representation.
- Parameterization: Kashin’s method was optimized over n and η, while δ remained fixed at 1.The paper notes that determining effective parameter values more easily requires further exploration.
- A.3 Dominance over Hadamard: Kashin’s representation dominates identity and randomized Hadamard across subsampling and quantization settings on the size–accuracy trade-off.It forms the Pareto frontier for all tested combinations.
B MNIST Experimental Results
The MNIST appendix evaluates lossy compression, Federated Dropout, and their combination. Figures 7–9 respectively report these experimental settings.
- Appendix placement: The MNIST experimental results are placed in the appendix for space reasons.
- Lossy compression: Figure 7 reports MNIST results for lossy compression under the experimental setup described in Section 4.2.
- Federated Dropout: Figure 8 reports MNIST results for Federated Dropout, whose details are given in Section 4.3.
- Combined methods: Figure 9 reports MNIST results combining lossy compression for downloads and uploads with Federated Dropout.