联合学习(FL)以来已提议已应用于许多领域,例如信用评估,医疗等。由于网络或计算资源的差异,客户端可能不会同时更新其渐变可能需要花费等待或闲置的时间。这就是为什么需要异步联合学习(AFL)方法。AFL中的主要瓶颈是沟通。如何在模型性能和通信成本之间找到平衡是AFL的挑战。本文提出了一种新的AFL框架VAFL。我们通过足够的实验验证了算法的性能。实验表明,VAFL可以通过48.23 \%的平均通信压缩速率降低约51.02 \%的通信时间,并允许模型更快地收敛。代码可用于\ url {https://github.com/robai-lab/vafl}
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With the development and progress of science and technology, the Internet of Things(IoT) has gradually entered people's lives, bringing great convenience to our lives and improving people's work efficiency. Specifically, the IoT can replace humans in jobs that they cannot perform. As a new type of IoT vehicle, the current status and trend of research on Unmanned Aerial Vehicle(UAV) is gratifying, and the development prospect is very promising. However, privacy and communication are still very serious issues in drone applications. This is because most drones still use centralized cloud-based data processing, which may lead to leakage of data collected by drones. At the same time, the large amount of data collected by drones may incur greater communication overhead when transferred to the cloud. Federated learning as a means of privacy protection can effectively solve the above two problems. However, federated learning when applied to UAV networks also needs to consider the heterogeneity of data, which is caused by regional differences in UAV regulation. In response, this paper proposes a new algorithm FedBA to optimize the global model and solves the data heterogeneity problem. In addition, we apply the algorithm to some real datasets, and the experimental results show that the algorithm outperforms other algorithms and improves the accuracy of the local model for UAVs.
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Unlike traditional distributed machine learning, federated learning stores data locally for training and then aggregates the models on the server, which solves the data security problem that may arise in traditional distributed machine learning. However, during the training process, the transmission of model parameters can impose a significant load on the network bandwidth. It has been pointed out that the vast majority of model parameters are redundant during model parameter transmission. In this paper, we explore the data distribution law of selected partial model parameters on this basis, and propose a deep hierarchical quantization compression algorithm, which further compresses the model and reduces the network load brought by data transmission through the hierarchical quantization of model parameters. And we adopt a dynamic sampling strategy for the selection of clients to accelerate the convergence of the model. Experimental results on different public datasets demonstrate the effectiveness of our algorithm.
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Federated Learning is a machine learning paradigm where we aim to train machine learning models in a distributed fashion. Many clients/edge devices collaborate with each other to train a single model on the central. Clients do not share their own datasets with each other, decoupling computation and data on the same device. In this paper, we propose yet another adaptive federated optimization method and some other ideas in the field of federated learning. We also perform experiments using these methods and showcase the improvement in the overall performance of federated learning.
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联合学习提供了以分布式方式学习异质用户数据的能力,同时保留用户隐私。但是,当前的客户选择技术是偏见的来源,因为它歧视了缓慢的客户。对于初学者,它选择满足某些网络和系统特定标准的客户端,从而选择慢速客户端。即使将这些客户包括在培训过程中,他们要么踩踏培训,要么因太慢而从回合中完全掉下来。我们提出的想法希望通过查看智能客户的选择和调度技术来找到快速融合和异质性之间的绝佳位置。
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我们介绍了一个新颖的联合学习框架FedD3,该框架减少了整体沟通量,并开放了联合学习的概念,从而在网络受限的环境中进行了更多的应用程序场景。它通过利用本地数据集蒸馏而不是传统的学习方法(i)大大减少沟通量,并(ii)将转移限制为一击通信,而不是迭代的多路交流来实现这一目标。 FedD3允许连接的客户独立提炼本地数据集,然后汇总那些去中心化的蒸馏数据集(通常以几个无法识别的图像,通常小于模型小于模型),而不是像其他联合学习方法共享模型更新,而是允许连接的客户独立提炼本地数据集。在整个网络上仅一次形成最终模型。我们的实验结果表明,FedD3在所需的沟通量方面显着优于其他联合学习框架,同时,根据使用情况或目标数据集,它为能够在准确性和沟通成本之间的权衡平衡。例如,要在具有10个客户的非IID CIFAR-10数据集上训练Alexnet模型,FedD3可以通过相似的通信量增加准确性超过71%,或者节省98%的通信量,同时达到相同的准确性与其他联合学习方法相比。
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Federated learning is a popular paradigm for machine learning. Ideally, federated learning works best when all clients share a similar data distribution. However, it is not always the case in the real world. Therefore, the topic of federated learning on heterogeneous data has gained more and more effort from both academia and industry. In this project, we first do extensive experiments to show how data skew and quantity skew will affect the performance of state-of-art federated learning algorithms. Then we propose a new algorithm FedMix which adjusts existing federated learning algorithms and we show its performance. We find that existing state-of-art algorithms such as FedProx and FedNova do not have a significant improvement in all testing cases. But by testing the existing and new algorithms, it seems that tweaking the client side is more effective than tweaking the server side.
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Federated learning is a collaborative model training method by iterating model updates at multiple clients and aggregation of the updates at a central server. Device and statistical heterogeneity of the participating clients cause performance degradation so that an appropriate weight should be assigned per client in the server's aggregation phase. This paper employs deep unfolding to learn the weights that adapt to the heterogeneity, which gives the model with high accuracy on uniform test data. The results of numerical experiments indicate the high performance of the proposed method and the interpretable behavior of the learned weights.
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由于参与客户的异构特征,联邦学习往往受到不稳定和缓慢的收敛。当客户参与比率低时,这种趋势加剧了,因为从每个轮的客户收集的信息容易更加不一致。为了解决挑战,我们提出了一种新的联合学习框架,这提高了服务器端聚合步骤的稳定性,这是通过将客户端发送与全局梯度估计的加速模型来引导本地梯度更新来实现的。我们的算法自然地聚合并将全局更新信息与没有额外的通信成本的参与者传达,并且不需要将过去的模型存储在客户端中。我们还规范了本地更新,以进一步降低偏差并提高本地更新的稳定性。我们根据各种设置执行了关于实际数据的全面实证研究,与最先进的方法相比,在准确性和通信效率方面表现出了拟议方法的显着性能,特别是具有低客户参与率。我们的代码可在https://github.com/ninigapa0 / fedagm获得
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联合学习(FL)是一个分布式的机器学习框架,可以减轻数据孤岛,在该筒仓中,分散的客户在不共享其私人数据的情况下协作学习全球模型。但是,客户的非独立且相同分布的(非IID)数据对训练有素的模型产生了负面影响,并且具有不同本地更新的客户可能会在每个通信回合中对本地梯度造成巨大差距。在本文中,我们提出了一种联合矢量平均(FedVeca)方法来解决上述非IID数据问题。具体而言,我们为与本地梯度相关的全球模型设定了一个新的目标。局部梯度定义为具有步长和方向的双向向量,其中步长为局部更新的数量,并且根据我们的定义将方向分为正和负。在FedVeca中,方向受步尺的影响,因此我们平均双向向量,以降低不同步骤尺寸的效果。然后,我们理论上分析了步骤大小与全球目标之间的关系,并在每个通信循环的步骤大小上获得上限。基于上限,我们为服务器和客户端设计了一种算法,以自适应调整使目标接近最佳的步骤大小。最后,我们通过构建原型系统对不同数据集,模型和场景进行实验,实验结果证明了FedVeca方法的有效性和效率。
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Federated Learning (FL) has been widely accepted as the solution for privacy-preserving machine learning without collecting raw data. While new technologies proposed in the past few years do evolve the FL area, unfortunately, the evaluation results presented in these works fall short in integrity and are hardly comparable because of the inconsistent evaluation metrics and experimental settings. In this paper, we propose a holistic evaluation framework for FL called FedEval, and present a benchmarking study on seven state-of-the-art FL algorithms. Specifically, we first introduce the core evaluation taxonomy model, called FedEval-Core, which covers four essential evaluation aspects for FL: Privacy, Robustness, Effectiveness, and Efficiency, with various well-defined metrics and experimental settings. Based on the FedEval-Core, we further develop an FL evaluation platform with standardized evaluation settings and easy-to-use interfaces. We then provide an in-depth benchmarking study between the seven well-known FL algorithms, including FedSGD, FedAvg, FedProx, FedOpt, FedSTC, SecAgg, and HEAgg. We comprehensively analyze the advantages and disadvantages of these algorithms and further identify the suitable practical scenarios for different algorithms, which is rarely done by prior work. Lastly, we excavate a set of take-away insights and future research directions, which are very helpful for researchers in the FL area.
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联合学习(FL)和分裂学习(SL)是两种新兴的协作学习方法,可能会极大地促进物联网(IoT)中无处不在的智能。联合学习使机器学习(ML)模型在本地培训的模型使用私人数据汇总为全球模型。分裂学习使ML模型的不同部分可以在学习框架中对不同工人进行协作培训。联合学习和分裂学习,每个学习都有独特的优势和各自的局限性,可能会相互补充,在物联网中无处不在的智能。因此,联合学习和分裂学习的结合最近成为一个活跃的研究领域,引起了广泛的兴趣。在本文中,我们回顾了联合学习和拆分学习方面的最新发展,并介绍了有关最先进技术的调查,该技术用于将这两种学习方法组合在基于边缘计算的物联网环境中。我们还确定了一些开放问题,并讨论了该领域未来研究的可能方向,希望进一步引起研究界对这个新兴领域的兴趣。
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Federated learning achieves joint training of deep models by connecting decentralized data sources, which can significantly mitigate the risk of privacy leakage. However, in a more general case, the distributions of labels among clients are different, called ``label distribution skew''. Directly applying conventional federated learning without consideration of label distribution skew issue significantly hurts the performance of the global model. To this end, we propose a novel federated learning method, named FedMGD, to alleviate the performance degradation caused by the label distribution skew issue. It introduces a global Generative Adversarial Network to model the global data distribution without access to local datasets, so the global model can be trained using the global information of data distribution without privacy leakage. The experimental results demonstrate that our proposed method significantly outperforms the state-of-the-art on several public benchmarks. Code is available at \url{https://github.com/Sheng-T/FedMGD}.
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高效联合学习是在边缘设备上培训和部署AI模型的关键挑战之一。然而,在联合学习中维护数据隐私提出了几种挑战,包括数据异质性,昂贵的通信成本和有限的资源。在本文中,我们通过(a)通过基于本地客户端的深度增强学习引入突出参数选择代理的上述问题,并在中央服务器上聚合所选择的突出参数,(b)分割正常的深度学习模型〜 (例如,CNNS)作为共享编码器和本地预测器,并通过联合学习训练共享编码器,同时通过本地自定义预测器将其知识传送到非IID客户端。所提出的方法(a)显着降低了联合学习的通信开销,并加速了模型推断,而方法(b)则在联合学习中解决数据异质性问题。此外,我们利用梯度控制机制来校正客户之间的梯度异质性。这使得训练过程更稳定并更快地收敛。实验表明,我们的方法产生了稳定的训练过程,并与最先进的方法相比实现了显着的结果。在培训VGG-11时,我们的方法明显降低了通信成本最高108 GB,并在培训Reset-20时需要7.6美元的通信开销,同时通过减少高达39.7 \%$ 39.7 \%$ vgg- 11.
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联合学习是一种在不违反隐私限制的情况下对分布式数据集进行统计模型培训统计模型的最新方法。通过共享模型而不是客户和服务器之间的数据来保留数据位置原则。这带来了许多优势,但也带来了新的挑战。在本报告中,我们探讨了这个新的研究领域,并执行了几项实验,以加深我们对这些挑战的理解以及不同的问题设置如何影响最终模型的性能。最后,我们为这些挑战之一提供了一种新颖的方法,并将其与文献中的其他方法进行了比较。
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The statistical heterogeneity of the non-independent and identically distributed (non-IID) data in local clients significantly limits the performance of federated learning. Previous attempts like FedProx, SCAFFOLD, MOON, FedNova and FedDyn resort to an optimization perspective, which requires an auxiliary term or re-weights local updates to calibrate the learning bias or the objective inconsistency. However, in addition to previous explorations for improvement in federated averaging, our analysis shows that another critical bottleneck is the poorer optima of client models in more heterogeneous conditions. We thus introduce a data-driven approach called FedSkip to improve the client optima by periodically skipping federated averaging and scattering local models to the cross devices. We provide theoretical analysis of the possible benefit from FedSkip and conduct extensive experiments on a range of datasets to demonstrate that FedSkip achieves much higher accuracy, better aggregation efficiency and competing communication efficiency. Source code is available at: https://github.com/MediaBrain-SJTU/FedSkip.
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联合学习(FL)作为边缘设备的有希望的技术,以协作学习共享预测模型,同时保持其训练数据,从而解耦了从需要存储云中的数据的机器学习的能力。然而,在规模和系统异质性方面,FL难以现实地实现。虽然有许多用于模拟FL算法的研究框架,但它们不支持在异构边缘设备上进行可扩展的流程。在本文中,我们呈现花 - 一种全面的FL框架,通过提供新的设施来执行大规模的FL实验并考虑丰富的异构流程来区分现有平台。我们的实验表明花卉可以仅使用一对高端GPU在客户尺寸下进行FL实验。然后,研究人员可以将实验无缝地迁移到真实设备中以检查设计空间的其他部分。我们认为花卉为社区提供了一个批判性的新工具,用于研究和发展。
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空中接入网络已被识别为各种事物互联网(物联网)服务和应用程序的重要驾驶员。特别是,以无人机互联网为中心的空中计算网络基础设施已经掀起了自动图像识别的新革命。这种新兴技术依赖于共享地面真理标记的无人机(UAV)群之间的数据,以培训高质量的自动图像识别模型。但是,这种方法将带来数据隐私和数据可用性挑战。为了解决这些问题,我们首先向一个半监督的联邦学习(SSFL)框架提供隐私保留的UAV图像识别。具体而言,我们提出了模型参数混合策略,以改善两个现实场景下的FL和半监督学习方法的天真组合(标签 - 客户端和标签 - 服务器),其被称为联合混合(FEDMIX)。此外,在不同环境中使用不同的相机模块,在不同环境中使用不同的相机模块,在不同的相机模块,即统计异质性,存在显着差异。为了减轻统计异质性问题,我们提出了基于客户参与训练的频率的聚合规则,即FedFReq聚合规则,可以根据其频率调整相应的本地模型的权重。数值结果表明,我们提出的方法的性能明显优于当前基线的性能,并且对不同的非IID等级的客户数据具有强大。
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联邦学习(FL)是利用属于患者,人,公司或行业的敏感数据的合适解决方案,这些数据在刚性隐私约束下工作的难题。 FL主要或部分地支持数据隐私和安全问题,并提供促进促进多个边缘设备或组织的模型问题的替代方案,以使用许多本地数据培训全局模型而不具有它们。由其分布式自然引起的FL的非IID数据具有显着的性能下降和稳定性偏斜。本文介绍了一种新颖的方法,通过增强图像动态平衡客户端的数据分布,以解决FL的非IID数据问题。介绍的方法非常稳定模型培训,并将模型的测试精度从83.22%提高到89.43%,对于高度IID FL设定中的胸部X射线图像的多胸疾病检测。 IID,非IID和非IID的结果,联合培训表明,该方法可能有助于鼓励组织或研究人员开发更好的系统,以获得与数据隐私的数据的价值不仅适用于医疗保健,而且领域。
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联合学习允许多个参与者在不公开数据隐私的情况下协作培训高效模型。但是,这种分布式的机器学习培训方法容易受到拜占庭客户的攻击,拜占庭客户通过修改模型或上传假梯度来干扰全球模型的训练。在本文中,我们提出了一种基于联邦学习(CMFL)的新型无服务器联合学习框架委员会机制,该机制可以确保算法具有融合保证的鲁棒性。在CMFL中,设立了一个委员会系统,以筛选上载已上传的本地梯度。 The committee system selects the local gradients rated by the elected members for the aggregation procedure through the selection strategy, and replaces the committee member through the election strategy.基于模型性能和防御的不同考虑,设计了两种相反的选择策略是为了精确和鲁棒性。广泛的实验表明,与典型的联邦学习相比,与传统的稳健性相比,CMFL的融合和更高的准确性比传统的稳健性,以分散的方法的方式获得了传统的耐受性算法。此外,我们理论上分析并证明了在不同的选举和选择策略下CMFL的收敛性,这与实验结果一致。
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