Privacy-Preserving Federated Neural Architecture Search With Enhanced Robustness for Edge Computing

Abstract:

With the development of large-scale artificial intelligence services, edge devices are becoming essential providers of data and computing power. However, these edge devices are not immune to malicious attacks. Federated learning (FL), while protecting privacy of decentralized data through secure aggregation, struggles to trace adversaries and lacks optimization for heterogeneity. We discover that FL augmented with Differentiable Architecture Search (DARTS) can improve resilience against backdoor attacks while compatible with secure aggregation. Based on this, we propose a federated neural architecture search (NAS) framwork named SLNAS. The architecture of SLNAS is built on three pivotal components: a server-side search space generation method that employs an evolutionary algorithm with dual encodings, a federated NAS process based on DARTS, and client-side architecture tuning that utilizes Gumbel softmax combined with knowledge distillation. To validate robustness, we adapt a framework that includes backdoor attacks based on trigger optimization, data poisoning, and model poisoning, targeting both model weights and architecture parameters. Extensive experiments demonstrate that SLNAS not only effectively counters advanced backdoor attacks but also handles heterogeneity, outperforming defense baselines across a wide range of backdoor attack scenarios.

edge computing

ABUV: Adaptive bitrate and upsampling for video streaming on mobile devices

Abstract

Fueled by the popularity of mobile devices, mobile channels have become the preferred video delivery medium. However, users often encounter a poor quality of experience (QoE) due to bandwidth limitations（带宽限制）, despite the implementation of adaptive bitrate (ABR) techniques. Recent advancements in super-resolution (SR) models have offered a potential solution to this situation, while the process of SR on mobile devices can introduce energy overhead and latency. To tackle these issues, we present ABUV, a system designed to enhance mobile video streaming by integrating adaptive bitrate and super-resolution technologies. ABUV leverages deep reinforcement learning to dynamically adjust both the bitrate and upsample decisions jointly based on considerations such as energy overhead and available bandwidth. It employs an optimized SR model specifically tailored for mobile devices, selectively applying the upsample process to chosen frames. Additionally, ABUV incorporates user-specific streaming information and adapts to the unique network environment through online training. In our experiments, we evaluate ABUV using real network traces and a diverse collection of videos, and the results show that ABUV can save up to 59% of data consumption and improve QoE by 27% compared to other video streaming systems.