Knowledge-Aware Federated Active Learning with Non-IID Data

Federated learning enables multiple decentralized clients to learn collaboratively without sharing the local training data. However, the expensive annotation cost to acquire data labels on local clients remains an obstacle in utilizing local data. In this paper, we propose a federated active learning paradigm to efficiently learn a global model with limited annotation budget while protecting data privacy in a decentralized learning way. The main challenge faced by federated active learning is the mismatch between the active sampling goal of the global model on the server and that of the asynchronous local clients. This becomes even more significant when data is distributed non-IID across local clients. To address the aforementioned challenge, we propose Knowledge-Aware Federated Active Learning (KAFAL), which consists of Knowledge-Specialized Active Sampling (KSAS) and Knowledge-Compensatory Federated Update (KCFU). KSAS is a novel active sampling method tailored for the federated active learning problem. It deals with the mismatch challenge by sampling actively based on the discrepancies between local and global models. KSAS intensifies specialized knowledge in local clients, ensuring the sampled data to be informative for both the local clients and the global model. KCFU, in the meantime, deals with the client heterogeneity caused by limited data and non-IID data distributions. It compensates for each client's ability in weak classes by the assistance of the global model. Extensive experiments and analyses are conducted to show the superiority of KSAS over the state-of-the-art active learning methods and the efficiency of KCFU under the federated active learning framework.Comment: 14 pages, 12 figure

Implementation of Active Learning Model with Integrated Digital Learning Media of Madura Local Culture

The low ability of students' cultural literacy in elementary schools is one of them influenced by the limitations of the teacher's ability to design active learning that integrates local culture. The low ability of students' cultural literacy in elementary schools is one of them influenced by the limitations of the teacher's ability to design active learning that integrates local culture. Rendahnya kemampuan literasi budaya siswa di sekolah dasar salah satunya dipengaruhi oleh keterbatasan kemampuan guru merancang pembelajaran aktif yang mengintegrasikan budaya lokal. The low ability of students' cultural literacy in elementary schools is influenced by the limited ability of teachers to design active learning that integrates local culture. Rendahnya kemampuan literasi budaya siswa di sekolah dasar dipengaruhi oleh keterbatasan kemampuan guru merancang pembelajaran aktif yang mengintegrasikan budaya lokal. Because it is necessary to implement an active learning learning model that is integrated through a digital learning media. This research is a type of descriptive research. The research subjects were grade 4 students at SDN Kamal 1 Bangkalan. Data collection techniques used are observation, tests, and questionnaires. Based on the results of the research, the implementation stages of the active learning learning model consist of planning, implementation, and evaluation stages through the application of an active learning model assisted by digital learning media integrated with the local culture of tanean lanjeng and roka' tase. The learning outcomes of student learning completeness reached 88% classically complete. Student responses during the learning process can be seen in the results of the student questionnaire showing a score of 85% with very interesting criteria. Student activity observation sheets are in the 80% category with active criteria. So it can be concluded that the application of active learning models assisted by digital learning media integrated with local culture is effective in learning in elementary schools

Robustness Analytics to Data Heterogeneity in Edge Computing

Federated Learning is a framework that jointly trains a model \textit{with} complete knowledge on a remotely placed centralized server, but \textit{without} the requirement of accessing the data stored in distributed machines. Some work assumes that the data generated from edge devices are identically and independently sampled from a common population distribution. However, such ideal sampling may not be realistic in many contexts. Also, models based on intrinsic agency, such as active sampling schemes, may lead to highly biased sampling. So an imminent question is how robust Federated Learning is to biased sampling? In this work\footnote{\url{https://github.com/jiaqian/robustness_of_FL}}, we experimentally investigate two such scenarios. First, we study a centralized classifier aggregated from a collection of local classifiers trained with data having categorical heterogeneity. Second, we study a classifier aggregated from a collection of local classifiers trained by data through active sampling at the edge. We present evidence in both scenarios that Federated Learning is robust to data heterogeneity when local training iterations and communication frequency are appropriately chosen

Active Improvement of Control Policies with Bayesian Gaussian Mixture Model

Learning from demonstration (LfD) is an intuitive framework allowing non-expert users to easily (re-)program robots. However, the quality and quantity of demonstrations have a great influence on the generalization performances of LfD approaches. In this paper, we introduce a novel active learning framework in order to improve the generalization capabilities of control policies. The proposed approach is based on the epistemic uncertainties of Bayesian Gaussian mixture models (BGMMs). We determine the new query point location by optimizing a closed-form information-density cost based on the quadratic R\'enyi entropy. Furthermore, to better represent uncertain regions and to avoid local optima problem, we propose to approximate the active learning cost with a Gaussian mixture model (GMM). We demonstrate our active learning framework in the context of a reaching task in a cluttered environment with an illustrative toy example and a real experiment with a Panda robot.Comment: Accepted for publication in IROS'2

Deformable appearance pyramids for anatomy representation, landmark detection and pathology classification

Purpose Representation of anatomy appearance is one of the key problems in medical image analysis. An appearance model represents the anatomies with parametric forms, which are then vectorised for prior learning, segmentation and classification tasks. Methods We propose a part-based parametric appearance model we refer to as a deformable appearance pyramid (DAP). The parts are delineated by multi-scale local feature pyramids extracted from an image pyramid. Each anatomy is represented by an appearance pyramid, with the variability within a population approximated by local translations of the multi-scale parts and linear appearance variations in the assembly of the parts. We introduce DAPs built on two types of image pyramids, namely Gaussian and wavelet pyramids, and present two approaches to model the prior and fit the model, one explicitly using a subspace Lucas–Kanade algorithm and the other implicitly using the supervised descent method (SDM). Results We validate the performance of the DAP instances with difference configurations on the problem of lumbar spinal stenosis for localising the landmarks and classifying the pathologies. We also compare them with classic methods such as active shape models, active appearance models and constrained local models. Experimental results show that the DAP built on wavelet pyramids and fitted with SDM gives the best results in both landmark localisation and classification. Conclusion A new appearance model is introduced with several configurations presented and evaluated. The DAPs can be readily applied for other clinical problems for the tasks of prior learning, landmark detection and pathology classification