ACM Transactions on Modeling and Performance Evaluation of Computing Systems

In data center networks, the reliability of Service Function Chain (SFC)—an end-to-end service presented by a chain of virtual network functions (VNFs)—is a complex and specific function of placement, configuration, and application requirements, both in hardware and software. Existing approaches to reliability analysis do not jointly consider multiple features of system components, including, (i) heterogeneity, (ii) disjointness, (iii) sharing, (iv) redundancy, and (v) failure interdependency. To this end, we develop a novel analysis of service reliability of the so-called generic SFC, consisting of n = k + r sub-SFCs, whereby k ≥ 1 and r ≥ 0 are the numbers of arbitrary placed primary and backup (redundant) sub-SFCs, respectively. Our analysis is based on combinatorics and a reduced binomial theorem—resulting in a simple approach, which, however, can be utilized to analyze rather complex SFC configurations. The analysis is practically applicable to various VNF placement strategies in arbitrary data center configurations, and topologies and can be effectively used for evaluation and optimization of reliable SFC placements.

Anna Engelmann

Admela Jukan

A Combinatorial Reliability Analysis of Generic Service Function Chains in Data Center Networks

数据中心网络中通用服务功能链的组合可靠性分析

Federated Learning (FL) has gained attention across various industries for its capability to train machine learning models without centralizing sensitive data. While this approach offers significant benefits such as privacy preservation and decreased communication overhead, it presents several challenges, including deployment complexity and interoperability issues, particularly in heterogeneous scenarios or resource-constrained environments. Over-the-air (OTA) FL was introduced to tackle these challenges by disseminating model updates without necessitating direct device-to-device connections or centralized servers. However, OTA-FL brought forth limitations associated with heightened energy consumption and network latency. In this paper, we propose a multi-attribute client selection framework employing the grey wolf optimizer (GWO) to strategically control the number of participants in each round and optimize the OTA-FL process while considering accuracy, energy, delay, reliability, and fairness constraints of participating devices. We evaluate the performance of our multi-attribute client selection approach in terms of model loss minimization, convergence time reduction, and energy efficiency. In our experimental evaluation, we assessed and compared the performance of our approach against the existing state-of-the-art methods. Our results demonstrate that the proposed GWO-based client selection outperforms these baselines across various metrics. Specifically, our approach achieves a notable reduction in model loss, accelerates convergence time, and enhances energy efficiency while maintaining high fairness and reliability indicators.

Maryam Ben Driss

Essaid Sabir

halima elbiaze

abdoulaye diallo

mohamed sadik

A Green Multi-Attribute Client Selection for Over-The-Air Federated Learning: A Grey-Wolf-Optimizer Approach

空中联邦学习中的绿色多属性客户端选择：灰狼优化方法

Federated Learning (FL) enables collaborative model training across decentralized edge devices while preserving data privacy. However, existing FL methods often assume clean annotated datasets, impractical for resource-constrained edge devices. In reality, noisy labels are prevalent, posing significant challenges to FL performance. Prior approaches attempt label correction and robust training techniques but exhibit limited efficacy, particularly under high noise levels. This paper introduces ClipFL ( F ederated L earning Cli ent P runing), a novel framework addressing noisy labels from a fresh perspective. ClipFL identifies and excludes noisy clients based on their performance on a clean validation dataset, tracked using a Noise Candidacy Score (NCS). The framework comprises three phases: pre-client pruning to identify potential noisy clients and calculate their NCS, client pruning to exclude a percentage of clients with the highest NCS, and post-client pruning for fine-tuning the global model with standard FL on clean clients. Empirical evaluation demonstrates ClipFL’s efficacy across diverse datasets and noise levels, achieving accurate noisy client identification, superior performance, faster convergence, and reduced communication costs compared to state-of-the-art FL methods. Our code is available at https://github.com/MMorafah/ClipFL.

Mahdi Morafah

Hojin Chang

Chen Chen

Bill Lin

Federated Learning Client Pruning for Noisy Labels

联邦学习客户端剪枝用于噪声标签

Communication overhead is a main bottleneck in federated learning (FL) especially in the wireless environment due to the limited data rate and unstable radio channels. The communication challenge necessitates holistic selection of participating clients that accounts for both the computation needs and communication cost, as well as judicious allocation of the limited transmission resource. Meanwhile, the random unpredictable nature of both the training data samples and the communication channels requires an online optimization approach that adapts to the changing system state over time. In this work, we consider a general framework of online joint client sampling and power allocation for wireless FL under time-varying communication channels. We formulate it as a stochastic network optimization problem that admits a Lyapunov-typed solution approach. This leads to per-training-round subproblems with a special bi-convex structure, which we leverage to propose globally optimal solutions, culminating in a meta algorithm that provides strong performance guarantees. We further study three specific FL problems covering multiple scenarios, namely with IID or non-IID data, whether robustness against data drift is required, and with unbiased or biased client sampling. We derive detailed algorithms for each of these problems. Simulation with standard classification tasks demonstrate that the proposed communication-aware algorithms outperform their counterparts under a wide range of learning and communication scenarios.

Wen Xu

Ben Liang

Gary Boudreau

Hamza Sokun

Clipper: Online Joint Client Sampling and Power Allocation for Wireless Federated Learning

Clipper: 无线联邦学习的在线联合客户端采样与功率分配

Federated Learning (FL) is a promising technique for decentralized privacy-preserving Machine Learning (ML) with a diverse pool of participating devices with varying device capabilities. However, existing approaches to handle such heterogeneous environments do not consider “fairness” in model aggregation, resulting in significant performance variation among devices. Meanwhile, prior works on FL fairness remain hardware-oblivious and cannot be applied directly without severe performance penalties. To address this issue, we propose a novel hardware-sensitive FL method called \(\mathsf {FairHetero} \) that promotes fairness among heterogeneous federated clients. Our approach offers tunable fairness within a group of devices with the same ML architecture as well as across different groups with heterogeneous models. Our evaluation under MNIST , FEMNIST , CIFAR10 , and SHAKESPEARE datasets demonstrates that \(\mathsf {FairHetero} \) can reduce variance among participating clients’ test loss compared to the existing state-of-the-art (SOTA) techniques, resulting in increased overall performance.

Zahidur Talukder

Bingqian Lu

Shaolei Ren

Mohammad Atiqul Islam

Hardware-Sensitive Fairness in Heterogeneous Federated Learning

异构联邦学习中的硬件敏感公平性

Model pruning has been proposed as a technique for reducing the size and complexity of Federated learning (FL) models. By making local models coarser, pruning is intuitively expected to improve protection against privacy attacks. However, the level of this expected privacy protection has not been previously characterized, or optimized jointly with utility. In this paper, we first characterize the privacy offered by pruning. We establish information-theoretic upper bounds on the information leakage from pruned FL and we experimentally validate them under state-of-the-art privacy attacks across different FL pruning schemes. Second, we introduce PriPrune – a privacy-aware algorithm for pruning in FL. PriPrune uses defense pruning masks, which can be applied locally after any pruning algorithm, and adapts the defense pruning rate to jointly optimize privacy and accuracy. Another key idea in the design of PriPrune is Pseudo-Pruning : it undergoes defense pruning within the local model and only sends the pruned model to the server; while the weights pruned out by defense mask are withheld locally for future local training rather than being removed. We show that PriPrune significantly improves the privacy-accuracy tradeoff compared to state-of-the-art pruned FL schemes. For example, on the FEMNIST dataset, PriPrune improves the privacy of PruneFL by 45.5% without reducing accuracy.

短名	ACM Trans. Model. Perform. Eval. Comput. Syst.
Journal Impact	0.72
国际分区	COMPUTER SCIENCE, INFORMATION SYSTEMS(Q4)

ISSN	2376-3639, 2376-3647
h-index	16

涉及主题	计算机科学工程类操作系统计算机网络数学程序设计语言经济物理分布式计算统计电信政治学法学万维网量子力学管理电气工程生物业务人工智能
出版信息	出版商: Association for Computing Machinery (ACM)，出版周期: ，期刊类型: journal，开源期刊: 否
基本数据	创刊年份: 2016，原创研究文献占比: 100.00%，自引率:14.30%， Gold OA占比: 0.00%

ACM Transactions on Modeling and Performance Evaluation of Computing Systems

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A Combinatorial Reliability Analysis of Generic Service Function Chains in Data Center Networks

A Green Multi-Attribute Client Selection for Over-The-Air Federated Learning: A Grey-Wolf-Optimizer Approach

Federated Learning Client Pruning for Noisy Labels

Clipper: Online Joint Client Sampling and Power Allocation for Wireless Federated Learning

Hardware-Sensitive Fairness in Heterogeneous Federated Learning

A Green Multi-Attribute Client Selection for Over-The-Air Federated Learning: A Grey-Wolf-Optimizer Approach

Federated Learning Client Pruning for Noisy Labels

Hardware-Sensitive Fairness in Heterogeneous Federated Learning

Clipper: Online Joint Client Sampling and Power Allocation for Wireless Federated Learning

PriPrune: Quantifying and Preserving Privacy in Pruned Federated Learning