One-Bit Over-the-Air Aggregation for Communication-Efficient Federated Edge Learning: Design and Convergence Analysis (2001.05713v2)

Published 16 Jan 2020 in cs.IT, cs.DC, cs.NI, eess.SP, and math.IT

Abstract: Federated edge learning (FEEL) is a popular framework for model training at an edge server using data distributed at edge devices (e.g., smart-phones and sensors) without compromising their privacy. In the FEEL framework, edge devices periodically transmit high-dimensional stochastic gradients to the edge server, where these gradients are aggregated and used to update a global model. When the edge devices share the same communication medium, the multiple access channel (MAC) from the devices to the edge server induces a communication bottleneck. To overcome this bottleneck, an efficient broadband analog transmission scheme has been recently proposed, featuring the aggregation of analog modulated gradients (or local models) via the waveform-superposition property of the wireless medium. However, the assumed linear analog modulation makes it difficult to deploy this technique in modern wireless systems that exclusively use digital modulation. To address this issue, we propose in this work a novel digital version of broadband over-the-air aggregation, called one-bit broadband digital aggregation (OBDA). The new scheme features one-bit gradient quantization followed by digital quadrature amplitude modulation (QAM) at edge devices and over-the-air majority-voting based decoding at edge server. We provide a comprehensive analysis of the effects of wireless channel hostilities (channel noise, fading, and channel estimation errors) on the convergence rate of the proposed FEEL scheme. The analysis shows that the hostilities slow down the convergence of the learning process by introducing a scaling factor and a bias term into the gradient norm. However, we show that all the negative effects vanish as the number of participating devices grows, but at a different rate for each type of channel hostility.

Authors (4)

Guangxu Zhu (88 papers)
Yuqing Du (28 papers)
Deniz Gunduz (506 papers)
Kaibin Huang (186 papers)

Citations (293)

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Summary

One-Bit Over-the-Air Aggregation for Communication-Efficient Federated Edge Learning: Insights and Implications

Federated Edge Learning (FEEL) has emerged as a central paradigm for enabling distributed machine learning model training directly on edge devices, including smartphones and IoT sensors. This approach fosters data privacy, as user data remains local instead of being uploaded to central servers. However, a significant communication bottleneck arises when these devices share a common wireless medium, necessitating novel solutions to improve efficiency. This paper by Zhu et al. ventures into this challenge, proposing One-Bit Broadband Digital Aggregation (OBDA), a promising solution to bolster FEEL systems' communication efficiency.

Core Proposition and Methodology

Traditional FEEL implementations face difficulty when dealing with communication overhead due to the need for continuously transmitting high-dimensional gradient updates back to an edge server. To tackle this, Zhu et al. introduced an innovative one-bit gradient quantization technique coupled with over-the-air majority-voting based decoding. This approach dramatically reduces the required communication bandwidth, facilitating gradient aggregation directly at the edge server using RF signal properties. Consequently, OBDA harnesses both digital modulation—particularly digital quadrature amplitude modulation (QAM)—and the inherent superposition feature of multiple access channels (MAC).

A meticulous convergence analysis reveals the impact of hostile wireless channel conditions, such as noise, fading, and estimation errors, on OBDA's performance. The findings indicate that while such conditions introduce scaling factors and bias terms affecting convergence speed, they become negligible as the number of participating devices increases.

Numerical Results and Key Insights

The authors substantiate their proposed methodology with robust experimental outcomes. For instance, implementing OBDA over fading channels with imperfect Channel State Information (CSI) shows how efficient gradient aggregation is, even under adverse conditions. The results not only validate the theoretical models but also demonstrate impressive resilience to channel hostilities when the workforce of participating devices grows.

Bold Propositions and Comparisons

OBDA significantly contrasts with traditional FEEL paradigms, especially when juxtaposed against more conventional digital Orthogonal Frequency-Division Multiple Access (OFDMA) systems and analog implementations like Broadband Analog Aggregation (BAA). OBDA maintains comparable convergence rates while markedly lowering communication latency, underscoring a strong trade-off between communication efficiency and learning accuracy.

Future Developments and Implications

Practically, OBDA furnishes a tangible pathway towards embedding federated learning within existing wireless infrastructure, given its compatibility with digital modulation schemes prevalent in modern systems. Theoretical advancements stemming from OBDA might unravel new optimization techniques for resource-constrained deep learning models, facilitating broader adaptability across diverse compute-limited edge environments.

Looking ahead, a nuanced exploration into multi-cellular FEEL with OBDA could address inter-cell interference, thereby refining communication heterogeneity across network layers. Moreover, broadening the application of OBDA to multi-task learning environments could further shrink communication budgets while preserving learning performance.

In conclusion, OBDA encapsulates an efficacious stride in bridging federated learning's communication bottlenecks, demonstrating a compelling synthesis of theoretical grounding and empirical success to extend FEEL's horizons across wireless networks with constrained bandwidth. The work sets the stage for future renderings in federated learning infrastructure, positing OBDA as a cornerstone technique in the digital modulation toolkit.

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