AFMeta: Asynchronous Federated Meta-learning with Temporally Weighted Aggregation

Liu, Sheng; Qu, Haohao; Chen, Qiyang; Jian, Weitao; Li, Rui; You, Linlin

doi:10.1109/smartworld-uic-atc-scalcom-digitaltwin-pricomp-metaverse56740.2022.00100

Cited by 7 publications

(2 citation statements)

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“…After experimental analysis with the CIFAR-10 data set, the maximum accuracy of a model is found to be 91.15%. [6]This paper proposes Asynchronous Federated Meta-learning with Temporary Aggregation of Weighted (AFMeta-TW), one of the first works of asynchronous mode of federated meta-learning. Inorder to enhance model aggregation, it measures the staleness associated with the local models.An approach of temporarily weighted aggregation is utilized to combat temporal heterogeneity effectively and efficiently.The system aims to build an initial model with heterogeneous clients asynchronously by reducing learning time and embracing a meta-model for classification for a considerable performance.…”

Section: IImentioning

confidence: 99%

F1 Score Based Weighted Asynchronous Federated Learning

Yarlagadda,

Tule,

Myada

2024

IJRASET

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The domain of federated learning has observed remarkable developments in recent years, enabling collaborative model training while preserving data privacy. This paper discusses several recent advancements in the field of federated learning, particularly in asynchronous and weighted federated learning. A novel approach within the federated learning paradigm titled "F1 Score Based Weighted Asynchronous Federated Learning" is introduced. The approach addresses issues of biased aggregation and device heterogeneity by assigning weights to devices based on their F1 scores, prioritizing those with superior performance in classification. Leveraging the asynchronous nature of federated learning, this approach enhances both resource efficiency and convergence speed. By incorporating F1 scores into the weighting mechanism, a balanced emphasis on precision and recall is achieved. It can optimize resource utilization, expediting the learning process by focusing on updates from devices with more accurate and valuable information. This approach can enhance collaborative model improvement while preserving data privacy in federated learning.

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Section: IImentioning

confidence: 99%

F1 Score Based Weighted Asynchronous Federated Learning

Yarlagadda,

Tule,

Myada

2024

IJRASET

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“…EL techniques enable the integration of multiple machine learning algorithms, trained on the same dataset [10][11][12]. A variety of ensembling techniques, facilitating the most efficient base learner combination of either the same (homogeneous) or different (heterogeneous) type has been developed.…”

Section: Introductionmentioning

confidence: 99%

Cultivating Ensemble Diversity through Targeted Injection of Synthetic Data: Path Loss Prediction Examples

Sotiroudis

2024

Electronics

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Machine Learning (ML)-based models are steadily gaining popularity. Their performance is determined from the amount and the quality of data used at their inputs, as well as from the competence and proper tuning of the ML algorithm used. However, collecting high-quality real data is time-consuming and expensive. Synthetic Data Generation (SDG) is therefore employed in order to augment the limited real data. Moreover, Ensemble Learning (EL) provides the framework to optimally combine a set of standalone ML algorithms (base learners), capitalizing on their individual strengths. Base learner diversity is essential to build a strong ensemble. The proposed method of Targeted Injection of Synthetic Data (TIoSD) combines the EL and SDG concepts in order to further diversify the base learners’ predictions, thus giving rise to an even stronger ensemble model. We have applied TIoSD in two different Path Loss (PL) datasets, using two well-established SDG methods (namely SMOGN and CTGAN). While the conventional ensemble model reached a Minimum Absolute Error (MAE) value of 3.25 dB, the TIoSD-triggered ensemble provided a MAE value of 3.16 dB. It is therefore concluded that targeted synthetic data injection, due to its diversity-triggering characteristics, enhances the ensemble’s performance. Moreover, the ratio between synthetic and real data has been investigated. The results showed that a proportion of 0.1 is optimal.

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