FedPAQ: A Communication-Efficient Federated Learning Method with Periodic Averaging and Quantization (1909.13014v4)

Abstract: Federated learning is a distributed framework according to which a model is trained over a set of devices, while keeping data localized. This framework faces several systems-oriented challenges which include (i) communication bottleneck since a large number of devices upload their local updates to a parameter server, and (ii) scalability as the federated network consists of millions of devices. Due to these systems challenges as well as issues related to statistical heterogeneity of data and privacy concerns, designing a provably efficient federated learning method is of significant importance yet it remains challenging. In this paper, we present FedPAQ, a communication-efficient Federated Learning method with Periodic Averaging and Quantization. FedPAQ relies on three key features: (1) periodic averaging where models are updated locally at devices and only periodically averaged at the server; (2) partial device participation where only a fraction of devices participate in each round of the training; and (3) quantized message-passing where the edge nodes quantize their updates before uploading to the parameter server. These features address the communications and scalability challenges in federated learning. We also show that FedPAQ achieves near-optimal theoretical guarantees for strongly convex and non-convex loss functions and empirically demonstrate the communication-computation tradeoff provided by our method.

FedPAQ: A Communication-Efficient Federated Learning Method with Periodic Averaging and Quantization

In the context of federated learning—a model that emphasizes decentralized data processing—managing communication overhead and scalability challenges remains a significant hurdle. The research paper presents FedPAQ, a federated learning algorithm designed to address these issues through three primary mechanisms: periodic averaging, partial device participation, and quantization.

Methodology

1. Periodic Averaging: In FedPAQ, local models are updated multiple times before their parameters are synchronized with the central server. This approach reduces the frequency of communication, thereby mitigating the communication bottleneck that is prevalent in federated learning systems.
2. Partial Device Participation: Only a subset of devices participate in each round of communication. This not only reduces the load on the network but also aligns with practical constraints where not all devices are consistently available or necessary for effective training.
3. Quantized Communication: Devices send a quantized version of the model updates to the server. This reduces the amount of data transmitted over the network without significantly impacting the accuracy of the model.

Theoretical Contributions

FedPAQ distinguishes itself by offering theoretical guarantees in both strongly convex and non-convex settings.

• In the strongly convex case, FedPAQ achieves a convergence rate of $\mathcal{O}(1/T)$, where $T$ is the total number of communication rounds. This signifies communication efficiency without sacrificing accuracy.
• For non-convex loss functions, the algorithm reaches a first-order stationary point at a rate of $\mathcal{O}(1/\sqrt{T})$, demonstrating its effectiveness in complex, non-linear learning environments.

Numerical Results

The paper empirically evaluates FedPAQ on applications such as logistic regression over the MNIST dataset and neural network training on CIFAR-10. Results highlight the communication-computation trade-offs, showing how tuning parameters like the period length and quantization levels can optimize the total training time while maintaining model performance.

Implications and Future Directions

FedPAQ's approach to reduce communication overhead is particularly beneficial as the number of participating devices scales. Its application can extend to various large-scale machine learning tasks where model training at the edge preserves data privacy and reduces latency. Future explorations could delve into adaptive mechanisms for deciding the subset of devices participating per round and explore more sophisticated quantization techniques to further reduce communication costs without compromising model fidelity.

Conclusion

By meticulously addressing both theoretical and practical challenges, the FedPAQ method contributes significantly to the landscape of federated learning. Its balance of communication efficiency and robust theoretical guarantees makes it a viable strategy for real-world applications demanding large-scale decentralized data processing. As federated learning continues to evolve, methodologies like FedPAQ that explicitly focus on communication efficiency will play a critical role in enabling scalable, efficient, and privacy-preserving machine learning systems.