On Efficient Federated Learning for Aerial Remote Sensing Image Classification: A Filter Pruning Approach

Song, Qipeng; Cao, Jingbo; Li, Yue; Gao, Xueru; Shangguan, Chengzhi; Liang, Linlin

doi:10.1007/978-981-99-8070-3_15

Cited by 3 publications

(3 citation statements)

References 11 publications

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“…We also evaluate HeteroSwitch with a realistic FL dataset, Flair (Song et al, 2022) -this dataset includes images collected by real end-users with more than one thousand device types. Since Flair targets multi-label classification, we compare the averaged-precision across device types and variance of HeteroSwtich with a baseline of FedAvg and prior works (i.e., q-FedAvg and FedProx).…”

Section: Evaluation Results and Analysismentioning

confidence: 99%

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FedGPO: Heterogeneity-Aware Global Parameter optimization for Efficient Federated Learning

Kim¹,

Wu²

2022

2022 IEEE International Symposium on Workload Characterization (IISWC)

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Federated Learning (FL) is a practical approach to train deep learning models collaboratively across user-end devices, protecting user privacy by retaining raw data on-device. In FL, participating user-end devices are highly fragmented in terms of hardware and software configurations. Such fragmentation introduces a new type of data heterogeneity in FL, namely system-induced data heterogeneity, as each device generates distinct data depending on its hardware and software configurations. In this paper, we first characterize the impact of system-induced data heterogeneity on FL model performance. We collect a dataset using heterogeneous devices with variations across vendors and performance tiers. By using this dataset, we demonstrate that system-induced data heterogeneity negatively impacts accuracy, and deteriorates fairness and domain generalization problems in FL. To address these challenges, we propose HeteroSwitch, which adaptively adopts generalization techniques (i.e., ISP transformation and SWAD) depending on the level of bias caused by varying HW and SW configurations. In our evaluation with a realistic FL dataset (FLAIR), HeteroSwitch reduces the variance of averaged precision by 6.3% across device types.

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Section: Evaluation Results and Analysismentioning

confidence: 99%

“…We evaluate HeteroSwitch with a realistic FL dataset, Flair (Song et al, 2022). The distribution of averaged precision (AP) across device types is shown in Table 6.…”

Section: Impact On Realistic Fl Datasetmentioning

confidence: 99%

FedGPO: Heterogeneity-Aware Global Parameter optimization for Efficient Federated Learning

Kim¹,

Wu²

2022

2022 IEEE International Symposium on Workload Characterization (IISWC)

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“…Cost: The cost to publish the statistics, for example, per datasheet, is low. There already various paper that can be used for reference what a datasheet for datasets may look like, for example, Song et al (2022) provide a datasheet in their supplementary files.…”

Section: C4 Disclosure Of the Training Data Propertiesmentioning

confidence: 99%

Overview of transparency and inspectability mechanisms to achieve accountability of artificial intelligence systems

Hauer,

Krafft,

Zweig

2023

Data & Policy

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Several governmental organizations all over the world aim for algorithmic accountability of artificial intelligence systems. However, there are few specific proposals on how exactly to achieve it. This article provides an extensive overview of possible transparency and inspectability mechanisms that contribute to accountability for the technical components of an algorithmic decision-making system. Following the different phases of a generic software development process, we identify and discuss several such mechanisms. For each of them, we give an estimate of the cost with respect to time and money that might be associated with that measure.

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Generative AI-based Land Cover Classification via Federated Learning CNNs: Sustainable Insights from UAV Imagery

Jockusch,

Hossain,

Imteaj

et al. 2024

2024 IEEE Conference on Technologies for Sustainability (SusTech)

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On Efficient Federated Learning for Aerial Remote Sensing Image Classification: A Filter Pruning Approach

Cited by 3 publications

References 11 publications

FedGPO: Heterogeneity-Aware Global Parameter optimization for Efficient Federated Learning

FedGPO: Heterogeneity-Aware Global Parameter optimization for Efficient Federated Learning

Overview of transparency and inspectability mechanisms to achieve accountability of artificial intelligence systems

Generative AI-based Land Cover Classification via Federated Learning CNNs: Sustainable Insights from UAV Imagery

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