Communication-Efficient Federated Deep Learning with Asynchronous Model Update and Temporally Weighted Aggregation (1903.07424v1)

Abstract: Federated learning obtains a central model on the server by aggregating models trained locally on clients. As a result, federated learning does not require clients to upload their data to the server, thereby preserving the data privacy of the clients. One challenge in federated learning is to reduce the client-server communication since the end devices typically have very limited communication bandwidth. This paper presents an enhanced federated learning technique by proposing a synchronous learning strategy on the clients and a temporally weighted aggregation of the local models on the server. In the asynchronous learning strategy, different layers of the deep neural networks are categorized into shallow and deeps layers and the parameters of the deep layers are updated less frequently than those of the shallow layers. Furthermore, a temporally weighted aggregation strategy is introduced on the server to make use of the previously trained local models, thereby enhancing the accuracy and convergence of the central model. The proposed algorithm is empirically on two datasets with different deep neural networks. Our results demonstrate that the proposed asynchronous federated deep learning outperforms the baseline algorithm both in terms of communication cost and model accuracy.

• The paper demonstrates that asynchronous updates differentiating DNN layers reduce communication overhead while preserving model performance.
• The study introduces temporally weighted aggregation that prioritizes recent model updates to boost convergence and accuracy.
• Experimental results on MNIST and HAR datasets validate improved scalability and robustness in non-IID, real-world federated environments.

Communication-Efficient Federated Deep Learning with Asynchronous Model Update and Temporally Weighted Aggregation

The paper "Communication-Efficient Federated Deep Learning with Asynchronous Model Update and Temporally Weighted Aggregation" presents advancements in federated learning by addressing two main challenges: reducing communication costs and improving learning performance of the central model. The authors propose a systematic approach that combines asynchronous model updates with temporally weighted aggregation, offering enhancements to the federated learning framework.

Key Contributions

1. Asynchronous Model Update Strategy: This approach differentiates between shallow and deep layers of deep neural networks (DNNs) on client devices. Parameters of the deep layers are synchronized less frequently compared to those of shallow layers. This selective frequency model update minimizes the communication load while maintaining model performance.
2. Temporally Weighted Aggregation: The aggregation method assigns varying weights to model updates based on their recency. More recent updates are prioritized, leveraging freshly learned features over older ones. This method ensures that the central model on the server benefits more from newly acquired local information, enhancing its convergence rate and accuracy.

Experimental Analysis

The researchers empirically tested their proposed methods using two datasets:

• MNIST for image classification using Convolutional Neural Networks (CNNs).
• Human Activity Recognition (HAR) using Long Short-Term Memory (LSTM) networks.

Parameters were carefully chosen to simulate real-world scenarios where data is non-IID, imbalanced, and distributed across a large number of clients. Key experimental findings include:

• Enhanced Model Accuracy and Reduced Communication: The asynchronous model update, when combined with temporally weighted aggregation, outperformed traditional federated averaging (FedAVG) in both learning accuracy and reductions in communication cost. Notably, the total reduction of communication was found significant in more demanding tasks like HAR.
• Influence of Model Parameters and Scalability: Investigations showed that the frequency of updates and choice of aggregation weighting (parameter a) significantly impact performance. Additionally, the proposed method scales effectively when the number of clients (K) increases, showcasing its robustness for real-world applications with large user bases.

Implications and Future Directions

The proposed method holds promising implications for federated learning, particularly in scenarios constrained by communication bandwidth and privacy concerns. By reducing communication overhead, this research supports the deployment of federated systems in bandwidth-limited environments like mobile edge computing or IoT networks.

From a theoretical angle, the use of asynchronous updates opens a dialogue about optimizing DNN layers differently according to their learning characteristics. This can further the understanding of neural network generalizability and feature representation.

For future research, the exploration of evolving client models and hyperparameters dynamically adapted to client capabilities or data characteristics can enhance both robustness and personalization of federated learning. This evolution presents a promising avenue for reducing communication costs while enhancing model adaptability and accuracy across heterogeneous environments.

In summary, this paper provides a substantive methodology for enhancing federated learning viability and efficiency through thoughtful modifications in update frequency and aggregation techniques, promising effective deployment across varied domains with stringent communication constraints.