Active Learning Based Federated Learning for Waste and Natural Disaster Image Classification
<p>The feasibility of Federated Learning (FL) is highly dependent on the training and inference capabilities of local models, which are subject to the availability of meaningful and annotated data. The availability of such data is in turn contingent on the tedious and time-consuming annotation...
محفوظ في:
| المؤلف الرئيسي: | |
|---|---|
| مؤلفون آخرون: | , , , , |
| منشور في: |
2020
|
| الموضوعات: | |
| الوسوم: |
إضافة وسم
لا توجد وسوم, كن أول من يضع وسما على هذه التسجيلة!
|
| _version_ | 1864513561273827328 |
|---|---|
| author | Lulwa Ahmed (16869936) |
| author2 | Kashif Ahmad (12592762) Naina Said (16869939) Basheer Qolomany (16855527) Junaid Qadir (16494902) Ala Al-Fuqaha (4434340) |
| author2_role | author author author author author |
| author_facet | Lulwa Ahmed (16869936) Kashif Ahmad (12592762) Naina Said (16869939) Basheer Qolomany (16855527) Junaid Qadir (16494902) Ala Al-Fuqaha (4434340) |
| author_role | author |
| dc.creator.none.fl_str_mv | Lulwa Ahmed (16869936) Kashif Ahmad (12592762) Naina Said (16869939) Basheer Qolomany (16855527) Junaid Qadir (16494902) Ala Al-Fuqaha (4434340) |
| dc.date.none.fl_str_mv | 2020-11-17T00:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1109/access.2020.3038676 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Active_Learning_Based_Federated_Learning_for_Waste_and_Natural_Disaster_Image_Classification/24015858 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Information and computing sciences Data management and data science Machine learning Training Training data Manuals Machine learning Inspection Collaborative work Data models Federated learning Deep learning Active learning CNNs LSTM Natural disasters Waste classification |
| dc.title.none.fl_str_mv | Active Learning Based Federated Learning for Waste and Natural Disaster Image Classification |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p>The feasibility of Federated Learning (FL) is highly dependent on the training and inference capabilities of local models, which are subject to the availability of meaningful and annotated data. The availability of such data is in turn contingent on the tedious and time-consuming annotation job that typically requires the manual analysis of training samples. Active Learning (AL) provides an alternative solution allowing a Machine Learning (ML) model to automatically choose and label the data from which it learns without involving manual inspection of each training sample. In this work, we explore how FL can benefit from unlabelled data available at each participating client using AL. To this aim, we propose an AL-based FL framework by employing and evaluating several AL methods in two different application domains. Through an extensive experimentation setup, we show that AL is equally useful in federated and centralized learning by achieving comparable results with manually labeled data using fewer samples without involving human annotators in collecting training data. We also demonstrated that the proposed method is dataset/application independent by evaluating the proposed method in two interesting applications, namely natural disaster analysis and waste classification, having different properties and challenges. Promising results are obtained on both applications resulting in comparable results against the best-case scenario where each sample is manually analyzed and annotated (Baseline 1), and improvement of 3.1% and 4% with best methods respectively over the training sets with irrelevant images on natural disaster and waste classification datasets (Baseline 2).</p><h2>Other Information</h2><p>Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0/legalcode" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2020.3038676" target="_blank">https://dx.doi.org/10.1109/access.2020.3038676</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_4310ccdf4b63e7b1062c5b71d1ae0a0f |
| identifier_str_mv | 10.1109/access.2020.3038676 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/24015858 |
| publishDate | 2020 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Active Learning Based Federated Learning for Waste and Natural Disaster Image ClassificationLulwa Ahmed (16869936)Kashif Ahmad (12592762)Naina Said (16869939)Basheer Qolomany (16855527)Junaid Qadir (16494902)Ala Al-Fuqaha (4434340)Information and computing sciencesData management and data scienceMachine learningTrainingTraining dataManualsMachine learningInspectionCollaborative workData modelsFederated learningDeep learningActive learningCNNsLSTMNatural disastersWaste classification<p>The feasibility of Federated Learning (FL) is highly dependent on the training and inference capabilities of local models, which are subject to the availability of meaningful and annotated data. The availability of such data is in turn contingent on the tedious and time-consuming annotation job that typically requires the manual analysis of training samples. Active Learning (AL) provides an alternative solution allowing a Machine Learning (ML) model to automatically choose and label the data from which it learns without involving manual inspection of each training sample. In this work, we explore how FL can benefit from unlabelled data available at each participating client using AL. To this aim, we propose an AL-based FL framework by employing and evaluating several AL methods in two different application domains. Through an extensive experimentation setup, we show that AL is equally useful in federated and centralized learning by achieving comparable results with manually labeled data using fewer samples without involving human annotators in collecting training data. We also demonstrated that the proposed method is dataset/application independent by evaluating the proposed method in two interesting applications, namely natural disaster analysis and waste classification, having different properties and challenges. Promising results are obtained on both applications resulting in comparable results against the best-case scenario where each sample is manually analyzed and annotated (Baseline 1), and improvement of 3.1% and 4% with best methods respectively over the training sets with irrelevant images on natural disaster and waste classification datasets (Baseline 2).</p><h2>Other Information</h2><p>Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0/legalcode" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2020.3038676" target="_blank">https://dx.doi.org/10.1109/access.2020.3038676</a></p>2020-11-17T00:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1109/access.2020.3038676https://figshare.com/articles/journal_contribution/Active_Learning_Based_Federated_Learning_for_Waste_and_Natural_Disaster_Image_Classification/24015858CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/240158582020-11-17T00:00:00Z |
| spellingShingle | Active Learning Based Federated Learning for Waste and Natural Disaster Image Classification Lulwa Ahmed (16869936) Information and computing sciences Data management and data science Machine learning Training Training data Manuals Machine learning Inspection Collaborative work Data models Federated learning Deep learning Active learning CNNs LSTM Natural disasters Waste classification |
| status_str | publishedVersion |
| title | Active Learning Based Federated Learning for Waste and Natural Disaster Image Classification |
| title_full | Active Learning Based Federated Learning for Waste and Natural Disaster Image Classification |
| title_fullStr | Active Learning Based Federated Learning for Waste and Natural Disaster Image Classification |
| title_full_unstemmed | Active Learning Based Federated Learning for Waste and Natural Disaster Image Classification |
| title_short | Active Learning Based Federated Learning for Waste and Natural Disaster Image Classification |
| title_sort | Active Learning Based Federated Learning for Waste and Natural Disaster Image Classification |
| topic | Information and computing sciences Data management and data science Machine learning Training Training data Manuals Machine learning Inspection Collaborative work Data models Federated learning Deep learning Active learning CNNs LSTM Natural disasters Waste classification |