Broadband Analog Aggregation for Low-Latency Federated Edge Learning (Extended Version) (1812.11494v3)

Abstract: The popularity of mobile devices results in the availability of enormous data and computational resources at the network edge. To leverage the data and resources, a new machine learning paradigm, called edge learning, has emerged where learning algorithms are deployed at the edge for providing fast and intelligent services to mobile users. While computing speeds are advancing rapidly, the communication latency is becoming the bottleneck of fast edge learning. To address this issue, this work is focused on designing a low latency multi-access scheme for edge learning. We consider a popular framework, federated edge learning (FEEL), where edge-server and on-device learning are synchronized to train a model without violating user-data privacy. It is proposed that model updates simultaneously transmitted by devices over broadband channels should be analog aggregated "over-the-air" by exploiting the superposition property of a multi-access channel. Thereby, "interference" is harnessed to provide fast implementation of the model aggregation. This results in dramatical latency reduction compared with the traditional orthogonal access (i.e., OFDMA). In this work, the performance of FEEL is characterized targeting a single-cell random network. First, due to power alignment between devices as required for aggregation, a fundamental tradeoff is shown to exist between the update-reliability and the expected update-truncation ratio. This motivates the design of an opportunistic scheduling scheme for FEEL that selects devices within a distance threshold. This scheme is shown using real datasets to yield satisfactory learning performance in the presence of high mobility. Second, both the multi-access latency of the proposed analog aggregation and the OFDMA scheme are analyzed. Their ratio, which quantifies the latency reduction of the former, is proved to scale almost linearly with device population.

• The paper presents a novel over-the-air analog aggregation method that significantly reduces communication latency in federated edge learning.
• It quantitatively analyzes critical tradeoffs like SNR-truncation and reliability-quantity that affect update accuracy and overall model performance.
• Experimental results demonstrate that the BAA scheme scales almost linearly with the number of devices, enhancing the efficiency of edge AI applications.

Overview of Broadband Analog Aggregation for Low-Latency Federated Edge Learning

The paper "Broadband Analog Aggregation for Low-Latency Federated Edge Learning" addresses a significant challenge in edge learning: the communication latency bottleneck. As devices generate a vast amount of data at the network edge, federated edge learning (FEEL) has emerged to harness this data while preserving privacy. The paper proposes a novel broadband analog aggregation (BAA) scheme that exploits the waveform-superposition property of multi-access channels to reduce communication latency during model updates.

Key Contributions

1. BAA Scheme Development: The authors introduce a multi-access scheme where update signals are simultaneously transmitted for "over-the-air" aggregation. This approach bypasses traditional orthogonal access methods like OFDMA, resulting in substantial latency reductions.
2. Communication-and-Learning Tradeoffs: Two fundamental tradeoffs are analyzed:
   • SNR-Truncation Tradeoff: The necessity of channel inversion for amplitude alignment introduces a tradeoff between receive signal-to-noise ratio (SNR) and the truncation ratio of model updates.
   • Reliability-Quantity Tradeoff: By confining transmissions to cell-interior devices, there is a tradeoff between SNR gain and the fraction of total data used, which impacts overall learning performance.
3. Latency Analysis: The paper mathematically quantifies the latency reduction, demonstrating that BAA's efficiency scales almost linearly with the number of devices, highlighting its suitability for dense networks.
4. Experimental Evaluation: The authors validate their theoretical findings with experiments on a neural network model for handwritten-digit recognition. The results corroborate the tradeoffs and showcase significant latency savings compared to OFDMA.
5. Security and Beamforming Extensions: The paper discusses extending BAA for robustness against adversarial attacks via spread spectrum techniques and improvements for cell-edge devices through beamforming strategies.

Implications and Future Directions

The proposed BAA framework offers a promising avenue to address communication bottlenecks in federated edge learning systems, particularly in environments with a large number of devices. By reducing communication latency, BAA enhances the scalability and practicality of deploying real-time edge AI applications.

The theoretical tradeoffs identified between receive SNR and truncation ratio, alongside reliability and data quantity, provide a foundational basis for further research on optimizing edge learning systems. Additionally, integrating BAA with other techniques such as MIMO and robust coding can further advance the framework's resilience and efficacy.

Future directions might explore BAA's application in broader network configurations, including multi-cell scenarios and diverse data distributions, to fully realize the potential of edge learning in various contexts.

In conclusion, this paper significantly contributes to federated learning by proposing a novel, efficient communication strategy that aligns well with the requirements of modern edge applications. The insights into tradeoffs and practical implementations pave the way for subsequent enhancements and optimizations in the domain of AI-driven mobile networks.