Computation Offloading and Service Caching in Heterogeneous MEC Wireless Networks

Zhang, Yongqiang; Kishk, Mustafa A.; Alouini, Mohamed‐Slim

doi:10.1109/tmc.2021.3136595

Cited by 11 publications

(1 citation statement)

References 57 publications

(35 reference statements)

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“…T HE merge of 5G and mobile edge computing [2]- [5] (MEC) has sparked a transformative shift in the Internet of Things [6]- [8] (IoT) ecosystem, paving the way for an era of distributed collective intelligence across multimedia platforms. This shift is marked by the widespread deployment of IoT devices-from smart home gadgets to sophisticated autonomous driving systems-each producing voluminous data that enriches the network's intelligence, significantly impacting everyday life with enhanced connectivity and smarter solutions.…”

Section: Introductionmentioning

confidence: 99%

A Personalized Federated Learning Framework Using Side Information for Heterogeneous Data Classification

Zhang

Wei

Wang

et al. 2022

2022 IEEE International Conference on Big Data (Big Data)

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Federated learning (FL) offers a privacy-centric distributed learning framework, enabling model training on individual clients and central aggregation without necessitating data exchange. Nonetheless, FL implementations often suffer from non-i.i.d. and long-tailed class distributions across mobile applications, e.g., autonomous vehicles, which leads models to overfitting as local training may converge to sub-optimal. In our study, we explore the impact of data heterogeneity on model bias and introduce an innovative personalized FL framework, Multi-level Personalized Federated Learning (MuPFL), which leverages the hierarchical architecture of FL to fully harness computational resources at various levels. This framework integrates three pivotal modules: Biased Activation Value Dropout (BAVD) to mitigate overfitting and accelerate training; Adaptive Cluster-based Model Update (ACMU) to refine local models ensuring coherent global aggregation; and Prior Knowledge-assisted Classifier Fine-tuning (PKCF) to bolster classification and personalize models in accord with skewed local data with shared knowledge. Extensive experiments on diverse real-world datasets for image classification and semantic segmentation validate that MuPFL consistently outperforms state-of-the-art baselines, even under extreme non-i.i.d. and long-tail conditions, which enhances accuracy by as much as 7.39% and accelerates training by up to 80% at most, marking significant advancements in both efficiency and effectiveness.

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