# DoCoFL: Downlink Compression for Cross-Device Federated Learning

arXiv: https://arxiv.org/abs/2302.00543 · HTML: https://mlsanity.com/paper/2302.00543/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- Builds on 44 of its 67 references that are in the map.
- First posted on arXiv 1 Feb 2023.

## Builds on (most cited first, 30 of 44)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Advances and Open Problems in Federated Learning](https://mlsanity.com/paper/1912.04977/) (2019)
- [Character-level Convolutional Networks for Text Classification](https://mlsanity.com/paper/1509.01626/) (2015)
- [Federated Learning: Strategies for Improving Communication Efficiency](https://mlsanity.com/paper/1610.05492/) (2016)
- [Adaptive Federated Optimization](https://mlsanity.com/paper/2003.00295/) (2020)
- [Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimization](https://mlsanity.com/paper/2007.07481/) (2020)
- [signSGD: Compressed Optimisation for Non-Convex Problems](https://mlsanity.com/paper/1802.04434/) (2018)
- [Federated Optimization: Distributed Machine Learning for On-Device Intelligence](https://mlsanity.com/paper/1610.02527/) (2016)
- [A Unified Theory of Decentralized SGD with Changing Topology and Local Updates](https://mlsanity.com/paper/2003.10422/) (2020)
- [Error Feedback Fixes SignSGD and other Gradient Compression Schemes](https://mlsanity.com/paper/1901.09847/) (2019)
- [TernGrad: Ternary Gradients to Reduce Communication in Distributed Deep Learning](https://mlsanity.com/paper/1705.07878/) (2017)
- [PowerSGD: Practical Low-Rank Gradient Compression for Distributed Optimization](https://mlsanity.com/paper/1905.13727/) (2019)
- [The Convergence of Sparsified Gradient Methods](https://mlsanity.com/paper/1809.10505/) (2018)
- [FetchSGD: Communication-Efficient Federated Learning with Sketching](https://mlsanity.com/paper/2007.07682/) (2020)
- [Federated Learning with Compression: Unified Analysis and Sharp Guarantees](https://mlsanity.com/paper/2007.01154/) (2020)
- [Better Theory for SGD in the Nonconvex World](https://mlsanity.com/paper/2002.03329/) (2020)
- [On Biased Compression for Distributed Learning](https://mlsanity.com/paper/2002.12410/) (2020)
- [Distributed Mean Estimation with Limited Communication](https://mlsanity.com/paper/1611.00429/) (2016)
- [EF21: A New, Simpler, Theoretically Better, and Practically Faster Error Feedback](https://mlsanity.com/paper/2106.05203/) (2021)
- [Asynchronous Parallel Stochastic Gradient for Nonconvex Optimization](https://mlsanity.com/paper/1506.08272/) (2015)
- [UVeQFed: Universal Vector Quantization for Federated Learning](https://mlsanity.com/paper/2006.03262/) (2020)
- [Convolutional neural networks with low-rank regularization](https://mlsanity.com/paper/1511.06067/) (2015)
- [DoubleSqueeze: Parallel Stochastic Gradient Descent with Double-Pass Error-Compensated Compression](https://mlsanity.com/paper/1905.05957/) (2019)
- [Natural Compression for Distributed Deep Learning](https://mlsanity.com/paper/1905.10988/) (2019)
- [Communication-efficient distributed SGD with Sketching](https://mlsanity.com/paper/1903.04488/) (2019)
- [MARINA: Faster Non-Convex Distributed Learning with Compression](https://mlsanity.com/paper/2102.07845/) (2021)
- [EDEN: Communication-Efficient and Robust Distributed Mean Estimation for Federated Learning](https://mlsanity.com/paper/2108.08842/) (2021)
- [DRIVE: One-bit Distributed Mean Estimation](https://mlsanity.com/paper/2105.08339/) (2021)
- [Communication-Efficient Distributed Blockwise Momentum SGD with Error-Feedback](https://mlsanity.com/paper/1905.10936/) (2019)

## Built on by (most cited first, 28 of 28)

- [Federated Full-Parameter Tuning of Billion-Sized Language Models with Communication Cost under 18 Kilobytes](https://mlsanity.com/paper/2312.06353/) (2023)
- [Momentum Provably Improves Error Feedback!](https://mlsanity.com/paper/2305.15155/) (2023)
- [THC: Accelerating Distributed Deep Learning Using Tensor Homomorphic Compression](https://mlsanity.com/paper/2302.08545/) (2023)
- [FedImpro: Measuring and Improving Client Update in Federated Learning](https://mlsanity.com/paper/2402.07011/) (2024)
- [LoCoDL: Communication-Efficient Distributed Learning with Local Training and Compression](https://mlsanity.com/paper/2403.04348/) (2024)
- [FuseFL: One-Shot Federated Learning through the Lens of Causality with Progressive Model Fusion](https://mlsanity.com/paper/2410.20380/) (2024)
- [Mitigating Unintended Memorization with LoRA in Federated Learning for LLMs](https://mlsanity.com/paper/2502.05087/) (2025)
- [Ferret: Federated Full-Parameter Tuning at Scale for Large Language Models](https://mlsanity.com/paper/2409.06277/) (2024)
- [Quantizing With Randomized Hadamard Transforms: Efficient Heuristic Now Proven](https://mlsanity.com/paper/2605.06014/) (2026)
- [Blockchain-empowered Federated Learning: Benefits, Challenges, and Solutions](https://mlsanity.com/paper/2403.00873/) (2024)
- [The Panaceas for Improving Low-Rank Decomposition in Communication-Efficient Federated Learning](https://mlsanity.com/paper/2505.23176/) (2025)
- [DynamiQ: Accelerating Gradient Synchronization using Compressed Multi-hop All-reduce](https://mlsanity.com/paper/2602.08923/) (2026)
- [Research in Collaborative Learning Does Not Serve Cross-Silo Federated Learning in Practice](https://mlsanity.com/paper/2510.12595/) (2025)
- [Optimal and Near-Optimal Adaptive Vector Quantization](https://mlsanity.com/paper/2402.03158/) (2024)
- [Efficient Federated Learning against Byzantine Attacks and Data Heterogeneity via Aggregating Normalized Gradients](https://mlsanity.com/paper/2408.09539/) (2024)
- [Mask-Encoded Sparsification: Mitigating Biased Gradients in Communication-Efficient Split Learning](https://mlsanity.com/paper/2408.13787/) (2024)
- [FedFetch: Faster Federated Learning with Adaptive Downstream Prefetching](https://mlsanity.com/paper/2504.15366/) (2025)
- [γ-FedHT: Stepsize-Aware Hard-Threshold Gradient Compression in Federated Learning](https://mlsanity.com/paper/2505.12479/) (2025)
- [Tight analyses of first-order methods with error feedback](https://mlsanity.com/paper/2506.05271/) (2025)
- [On the Fast Adaptation of Delayed Clients in Decentralized Federated Learning: A Centroid-Aligned Distillation Approach](https://mlsanity.com/paper/2508.02993/) (2025)
- [FedBiF: Communication-Efficient Federated Learning via Bits Freezing](https://mlsanity.com/paper/2509.10161/) (2025)
- [H+: An Efficient Similarity-Aware Aggregation for Byzantine Resilient Federated Learning](https://mlsanity.com/paper/2509.24330/) (2025)
- [Beyond Communication Overhead: A Multilevel Monte Carlo Approach for Mitigating Compression Bias in Distributed Learning](https://mlsanity.com/paper/2507.05508/) (2025)
- [Dimensionality Reduction for Robust Federated Learning: A Theoretical Analysis and Convergence Guarantee](https://mlsanity.com/paper/2605.28335/) (2026)
- [From Data Heterogeneity to Convergence: A Data-Centric Review of Federated Learning](https://mlsanity.com/paper/2606.10595/) (2026)
- [DG-FedReuse: Proxy-Gradient-Gated Cached-Update Reuse with Matched Sparse Uplink Accounting](https://mlsanity.com/paper/2608.05358/) (2026)
- [TORQUE: Optimizing What (not) to Quantize Before and After Rotation](https://mlsanity.com/paper/2609.36032/) (2026)
- [Fast Quantized Johnson-Lindenstrauss Transforms](https://mlsanity.com/paper/2610.16289/) (2026)

## Abstract (arXiv)

Many compression techniques have been proposed to reduce the communication overhead of Federated Learning training procedures. However, these are typically designed for compressing model updates, which are expected to decay throughout training. As a result, such methods are inapplicable to downlink (i.e., from the parameter server to clients) compression in the cross-device setting, where heterogeneous clients textitmay appear only once during training and thus must download the model parameters. Accordingly, we propose textsfDoCoFL -- a new framework for downlink compression in the cross-device setting. Importantly, textsfDoCoFL can be seamlessly combined with many uplink compression schemes, rendering it suitable for bi-directional compression. Through extensive evaluation, we show that textsfDoCoFL offers significant bi-directional bandwidth reduction while achieving competitive accuracy to that of a baseline without any compression.