Fast Federated Learning by Balancing Communication Trade-Offs (2105.11028v1)

Abstract: Federated Learning (FL) has recently received a lot of attention for large-scale privacy-preserving machine learning. However, high communication overheads due to frequent gradient transmissions decelerate FL. To mitigate the communication overheads, two main techniques have been studied: (i) local update of weights characterizing the trade-off between communication and computation and (ii) gradient compression characterizing the trade-off between communication and precision. To the best of our knowledge, studying and balancing those two trade-offs jointly and dynamically while considering their impacts on convergence has remained unresolved even though it promises significantly faster FL. In this paper, we first formulate our problem to minimize learning error with respect to two variables: local update coefficients and sparsity budgets of gradient compression who characterize trade-offs between communication and computation/precision, respectively. We then derive an upper bound of the learning error in a given wall-clock time considering the interdependency between the two variables. Based on this theoretical analysis, we propose an enhanced FL scheme, namely Fast FL (FFL), that jointly and dynamically adjusts the two variables to minimize the learning error. We demonstrate that FFL consistently achieves higher accuracies faster than similar schemes existing in the literature.

• The paper proposes Fast FL (FFL), a novel scheme that dynamically balances communication-computation and communication-precision trade-offs to accelerate federated learning convergence.
• FFL uses a theoretically derived error bound to dynamically adjust local update coefficients and gradient sparsity budgets in each round for optimal speed.
• Empirical validation shows FFL significantly improves convergence speed over prior methods, benefiting bandwidth-constrained applications like IoT.

Fast Federated Learning by Balancing Communication Trade-Offs

The paper "Fast Federated Learning by Balancing Communication Trade-Offs" addresses the critical issue of communication overhead in Federated Learning (FL), a paradigm designed for privacy-preserving machine learning over distributed networks. FL typically encounters delays due to frequent gradient transmissions between distributed workers and a central server, which can significantly decelerate the learning process. The authors propose a novel approach aimed at jointly addressing two primary trade-offs inherent in FL: communication-computation and communication-precision. Their objective is to dynamically balance these trade-offs to achieve faster convergence of FL.

Problem Formulation

The work begins by formally defining the problem of optimizing FL to minimize learning error within a fixed wall-clock time. The optimization variables at play are the local update coefficients and the sparsity budgets for gradient compression. These coefficients and budgets respectively dictate the ratio between local computations versus communication and the precision of the gradients transmitted, thus characterizing the core trade-offs in FL.

Theoretical Analysis

The authors derive a theoretical upper bound of learning error as a function of the two optimization variables. This upper bound is essential for understanding the dynamics of the trade-offs involved and forms the basis for the proposed scheme. The analysis is rooted in the principles of smoothness and convexity of the loss function, unbiased gradient estimation, and bounded variance, all standard assumptions in federated learning contexts.

The work builds upon prior techniques for local updates and gradient compression but distinguishes itself by joining these methods into a cohesive framework. By extending the analysis with the concept of unbiased atomic decomposition, the authors describe how compression can be effectively applied to gradient matrices, considering their inherent sparsity.

Proposed Scheme: Fast FL (FFL)

The paper presents the Fast FL (FFL) scheme, which dynamically adjusts the local update coefficients and sparsity budgets in each round of FL. The decision-making process relies on minimizing the derived error upper bound. FFL thus optimizes these variables jointly to accelerate convergence.

Empirical validation demonstrates that FFL consistently improves convergence speed compared to state-of-the-art methods, such as ADACOMM, which uses dynamic adjustments for local updates without compression, and ATOMO, which applies static gradient compression without dynamic local updates. Results show that FFL significantly reduces the time to achieve high accuracy across varying communication scenarios, including differing data rates and network conditions.

Implications and Future Directions

This work provides a crucial step in enhancing the efficiency and practicality of federated learning, particularly for IoT applications where bandwidth constraints are prominent. By enabling dynamic adjustments based on the real-time convergence state, FFL could offer substantial improvements for personalized learning models distributed across edge devices.

Looking ahead, integrating FFL with advanced neural architectures and exploring the robustness of its dynamics in non-IID environments and under adversarial constraints could significantly impact its applicability in real-world scenarios. Furthermore, leveraging this strategy in conjunction with larger, more diverse datasets and edge computing paradigms presents an exciting horizon for future research in federated learning and distributed AI systems.