Source-linked AI summary
Communication-Efficient Federated Learning with Compensated Overlap-FedAvg
Yuhao Zhou, Ye Qing, Jiancheng Lv
TL;DR
Federated learning avoids sharing local datasets but suffers from communication overhead caused by frequent model exchanges. Overlap-FedAvg overlaps communication with computation and supplements this design with compensation and acceleration mechanisms. The paper reports faster federated learning while maintaining nearly the same model performance, with broad compatibility with compression methods.
Problem
Federated learning protects local data but has low communication efficiency because client-server communication occurs at the end of every training iteration.
Method
Overlap-FedAvg overlaps model communication with computation and uses hierarchical computing, data compensation, and NAG to address staleness and accelerate training.
Results
Overlap-FedAvg considerably speeds up federated learning while maintaining nearly the same model performance as vanilla FedAvg.
Takeaways & Limitations
The framework provides a communication-efficient structural approach that can be combined with many other data compression methods.
Takeaways & Limitations
Selecting the compensation hyper-parameter λ requires comprehensive environmental sampling, which is costly in time and computational resources.
Abstract
from arXiv · showhide
Petabytes of data are generated each day by emerging Internet of Things (IoT), but only few of them can be finally collected and used for Machine Learning (ML) purposes due to the apprehension of data & privacy leakage, which seriously retarding ML's growth. To alleviate this problem, Federated learning is proposed to perform model training by multiple clients' combined data without the dataset sharing within the cluster. Nevertheless, federated learning introduces massive communication overhead as the synchronized data in each epoch is of the same size as the model, and thereby leading to a low communication efficiency. Consequently, variant methods mainly focusing on the communication rounds reduction and data compression are proposed to reduce the communication overhead of federated learning. In this paper, we propose Overlap-FedAvg, a framework that parallels the model training phase with model uploading & downloading phase, so that the latter phase can be totally covered by the former phase. Compared to vanilla FedAvg, Overlap-FedAvg is further developed with a hierarchical computing strategy, a data compensation mechanism and a nesterov accelerated gradients~(NAG) algorithm. Besides, Overlap-FedAvg is orthogonal to many other compression methods so that they can be applied together to maximize the utilization of the cluster. Furthermore, the theoretical analysis is provided to prove the convergence of the proposed Overlap-FedAvg framework. Extensive experiments on both conventional and recurrent tasks with multiple models and datasets also demonstrate that the proposed Overlap-FedAvg framework substantially boosts the federated learning process.
1 INTRODUCTION
Federated learning enables collaborative model training without sharing clients’ local datasets, but repeated model communication creates substantial overhead. Overlap-FedAvg addresses this bottleneck by overlapping communication with computation and adding compensation, acceleration, and compatibility with compression methods.
- Motivation: Federated learning combines clients’ data for model training without revealing local datasets to the server or other participants.Clients share information such as model weights or gradients instead of plainly sharing their data.
- Motivation: Communication at every training iteration can transfer data comparable in size to the model, producing low efficiency and potentially petabyte-scale traffic.Large models and many iterations make the communication phase especially time-consuming.
- Related approaches: Existing communication-cost reductions enlarge communication intervals or compress transmitted data, but longer intervals can reduce accuracy and compression itself can be costly.Compression overhead is particularly relevant for battery-sensitive or low-performance devices.
- Proposed framework: The framework adds hierarchical computing, gradient or data compensation for staleness, and NAG acceleration, while remaining compatible with other compression methods.Taylor expansion supports compensation, and the paper also provides convergence analysis.
- Evaluation: Experiments on four benchmark datasets and eight DNN models report improved accuracy and reduced communication cost relative to FedAvg.The study evaluates the proposed framework against the baseline algorithm FedAvg.
2 LITERATURE REVIEW
Federated learning avoids sharing local datasets but incurs substantial communication overhead. Prior approaches reduce rounds or compress data, while Overlap-FedAvg overlaps communication and computation and adapts its communication interval to environmental conditions.
- Federated learning trains on clients’ combined datasets without revealing their local data to the central server or other participants.
- Existing communication-efficiency methods primarily reduce communication rounds or compress transferred data.
- Reducing communication rounds: FedAvg trades accuracy against efficiency through its manually tuned communication interval E: smaller E generally improves accuracy, while larger E generally accelerates convergence.
- Overlap-FedAvg overlaps communication with computation and automatically adapts E to network conditions through hierarchical computing.
3 METHODOLOGY
Overlap-FedAvg decouples local training from model transmission using separate processes, allowing computation to continue while communication proceeds. It addresses stale updates with compensation and supports NAG acceleration, while dynamically determining the communication interval.
- 3.2 Overlapping of the Communication: Separate training and communication processes let clients continue local model training while model uploading and downloading occur concurrently.
- 3.2 Overlapping of the Communication: Overlap-FedAvg dynamically chooses E according to communication and training times rather than requiring a manually configured constant.
- 3.2 Overlapping of the Communication: Self updates are used before new global weights arrive, while global updates are used after the required weights are received.
- 3.3 Gradients Compensation: Communication delays can produce stale models and gradients because the server may receive models from several iterations earlier.
- 3.3 Gradients Compensation: The compensation mechanism approximates current gradients from stale weights using Taylor expansion and a Fisher-information-based Hessian approximation.
- 3.3 Gradients Compensation: After gradient abstraction and estimation, NAG can be applied to accelerate Overlap-FedAvg training.
4 THEORETICAL ANALYSIS
The theoretical analysis formulates Overlap-FedAvg’s optimization problem under smoothness and convexity assumptions. Its theorems bound approximation effects and show that learning-rate decay makes the introduced error terms negligible for convergence.
- The analysis studies Overlap-FedAvg as a generic optimization problem with client-local objective functions and aggregation weights.
- The proofs assume client objectives are L1-smooth, gradients are L2-smooth, and objectives are strongly convex.
- Theorem 4.1 bounds the difference between real and approximated gradients under bounded gradient norms and diagonalization error.
- Theorem 4.2 derives a revised convergence lemma for Overlap-FedAvg under the stated assumptions and a learning-rate constraint.
- With a decaying learning rate, terms introduced by Overlap-FedAvg approach zero and become negligible for convergence on IID or Non-IID data.
5 EXPERIMENTS
The experiments evaluate Overlap-FedAvg across Non-IID vision and NLP settings, models, and datasets, comparing accuracy, convergence, and training speed with vanilla FedAvg. Results indicate that compensation and NAG address staleness while overlapping communication with computation reduces iteration time.
- Comparison of accuracy: Overlap-FedAvg adaptively adjusted communication intervals according to network and hardware conditions, demonstrating hierarchical-strategy adaptability on lightweight models.For lightweight models, the interval could be reduced when the fixed upper limit of five was too large for the current environment.
- Comparison of accuracy: NAG with compensated gradients substantially accelerated convergence compared with vanilla FedAvg.The experiments fixed λ = 0.2 while varying β across several DNN models.
- Comparison of accuracy: With larger models, stale parameters reduced accuracy when λ = 0, while proper data compensation greatly reduced the gap relative to vanilla FedAvg.Overlap-FedAvg still saved model-uploading and model-downloading time in these settings.
- Comparison of accuracy: On Wikitext-2, Overlap-FedAvg achieved nearly the same convergence speed by global iteration and a very similar final accuracy to the comparison setting.Performance was measured with Transformer perplexity, where lower values are better.
- Comparison of Training Speed: Overlap-FedAvg lowered wall-clock time in every experiment: approximately 10% for lightweight models and at most 40% for heavier models.The reported time savings increased as model parameter size increased.
- Comparison of Training Speed: Hardware utilization was much higher with Overlap-FedAvg than with vanilla FedAvg, whose utilization remained nearly fixed at 100%.The comparison concerns utilization during the federated-learning process.
6 DISCUSSION
The discussion examines whether Overlap-FedAvg's use of gradients creates privacy risks. It concludes that scaled gradients can support compensation without revealing clients' learning rates to the central server.
- Privacy Security of Overlap-FedAvg: A curious central server could use client updates and a known learning rate to reconstruct gradients and infer hidden training data.The discussion identifies gradient-based reconstruction as a privacy concern for federated learning.
- Privacy Security of Overlap-FedAvg: Clients can generate their own learning rates, reducing the central server's ability to recover real gradients from model weights.The proposed privacy discussion relies on the learning rate not being directly involved in vanilla FedAvg's global-model update.
- Privacy Security of Overlap-FedAvg: Overlap-FedAvg uses scaled rather than real gradients, allowing the server to assume a learning rate of 1.0 for compensation.For client learning rate ηc, the scaled gradient is defined as ηc∇F(wt−1)real.
- Privacy Security of Overlap-FedAvg: The compensated scaled gradients can be used in SGD while preserving the unknown client learning rate from the central server.The paper states that this preserves Overlap-FedAvg's privacy security without leaking the learning rate.
7 CONCLUSIONS AND FUTURE WORK
Overlap-FedAvg targets communication overhead in federated learning by structurally paralleling training and communication, while adding compensation and acceleration mechanisms. Experiments report faster learning with nearly unchanged performance in one configuration and substantially higher accuracy with NAG, but selecting λ remains costly.
- Federated learning incurs massive communication overhead from transmitting heavy DNN weights over low-bandwidth connections.
- Overlap-FedAvg parallels model training with communication to enable communication-efficient federated learning.The framework is designed from a structural perspective.
- Parallelism and data compensation considerably speed up federated learning while maintaining nearly the same final accuracy as FedAvg.This result is reported for Overlap-FedAvg without NAG.
- With NAG enabled, Overlap-FedAvg massively surpasses vanilla FedAvg in accuracy across both training iterations and wall-clock time.The reported advantage is especially relevant for relatively large models and slow or unstable client bandwidth.
- The data compensation mechanism introduces λ, whose optimal selection requires costly environmental sampling of time and computational resources.Future work proposes adaptive selection using evolutionary algorithms or reinforcement learning.