Towards Energy-efficient Federated Learning via INT8-based Training on Mobile DSPs

Jinliang Yuan, Shangguang Wang, hongyu li, Daliang Xu, Yuanchun Li, Mengwei Xu, Xuanzhe Liu

Keywords: Mobile computing, federated learning, energy efficiency

Abstract:

AI is making the Web an even cooler place, but also introduces serious privacy risks due to the extensive user data collection. Federated
learning (FL), as a privacy-preserving machine learning paradigm,
enables mobile devices to collaboratively learn a shared prediction
model while keeping all training data on devices. However, a key
obstacle towards practical cross-device FL training is huge energy
consumption, especially for lightweight mobile devices.
In this work, we perform the first-of-its-kind analysis of im-
proving FL performance through low-precision training with an
energy-friendly Digital Signal Processor (DSP) on mobile devices.
We first demonstrate that directly integrating the state-of-the-art
INT8 (8-bit integer) training algorithm and classic FL protocols will
significantly degrade the model accuracy. Moreover, we observe
that there are still unavoidable frequent quantization operations on
devices that cause extreme load stress on DSP-enabled INT8 training. To address the above challenges, we present Q-FedUpdate, an
FL framework that efficiently preserves model accuracy with ultra-
low energy consumption. It maintains a global full-precision model
and allows the tiny model updates to be continuously accumulated,
instead of being erased by the quantization. Furthermore, it intro-
duces pipelining technology to parallel CPU-based quantization and
DSP-enabled training, which reduces the floating-point computation overhead of frequent data quantization. Extensive experiments
show that Q-FedUpdate can effectively reduce the on-device energy consumption by 21×, and accelerate the FL convergence by
6.1× with only 2% accuracy loss.

Track: Systems and Infrastructure for Web, Mobile, and WoT

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Submission Number: 158