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Register a new userDeveloping a Collaborative and Autonomous Training and Learning Environment for Hybrid Wireless Networks
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Added by
Matthias Brust R.
Publication date
2007
fields
Informatics Engineering
and research's language is
English
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With larger memory capacities and the ability to link into wireless networks, more and more students uses palmtop and handheld computers for learning activities. However, existing software for Web-based learning is not well-suited for such mobile devices, both due to constrained user interfaces as well as communication effort required. A new generation of applications for the learning domain that is explicitly designed to work on these kinds of small mobile devices has to be developed. For this purpose, we introduce CARLA, a cooperative learning system that is designed to act in hybrid wireless networks. As a cooperative environment, CARLA aims at disseminating teaching material, notes, and even components of itself through both fixed and mobile networks to interested nodes. Due to the mobility of nodes, CARLA deals with upcoming problems such as network partitions and synchronization of teaching material, resource dependencies, and time constraints.
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Programming education is becoming important as demands on computer literacy and coding skills are growing. Despite the increasing popularity of interactive online learning systems, many programming courses in schools have not changed their teaching format from the conventional classroom setting. We see two research opportunities here. Students may have diverse expertise and experience in programming. Thus, particular content and teaching speed can be disengaging for experienced students or discouraging for novice learners. In a large classroom, instructors cannot oversee the learning progress of each student, and have difficulty matching teaching materials with the comprehension level of individual students. We present ClassCode, a web-based environment tailored to programming education in classrooms. Students can take online tutorials prepared by instructors at their own pace. They can then deepen their understandings by performing interactive coding exercises interleaved within tutorials. ClassCode tracks all interactions by each student, and summarizes them to instructors. This serves as a progress report, facilitating the instructors to provide additional explanations in-situ or revise course materials. Our user evaluation through a small lecture and expert review by instructors and teaching assistants confirm the potential of ClassCode by uncovering how it could address issues in existing programming courses at universities.
Facial recognition is a key enabling component for emerging Internet of Things (IoT) services such as smart homes or responsive offices. Through the use of deep neural networks, facial recognition has achieved excellent performance. However, this is only possibly when trained with hundreds of images of each user in different viewing and lighting conditions. Clearly, this level of effort in enrolment and labelling is impossible for wide-spread deployment and adoption. Inspired by the fact that most people carry smart wireless devices with them, e.g. smartphones, we propose to use this wireless identifier as a supervisory label. This allows us to curate a dataset of facial images that are unique to a certain domain e.g. a set of people in a particular office. This custom corpus can then be used to finetune existing pre-trained models e.g. FaceNet. However, due to the vagaries of wireless propagation in buildings, the supervisory labels are noisy and weak.We propose a novel technique, AutoTune, which learns and refines the association between a face and wireless identifier over time, by increasing the inter-cluster separation and minimizing the intra-cluster distance. Through extensive experiments with multiple users on two sites, we demonstrate the ability of AutoTune to design an environment-specific, continually evolving facial recognition system with entirely no user effort.
There is an increasing interest in a fast-growing machine learning technique called Federated Learning, in which the model training is distributed over mobile user equipments (UEs), exploiting UEs local computation and training data. Despite its advantages in data privacy-preserving, Federated Learning (FL) still has challenges in heterogeneity across UEs data and physical resources. We first propose a FL algorithm which can handle the heterogeneous UEs data challenge without further assumptions except strongly convex and smooth loss functions. We provide the convergence rate characterizing the trade-off between local computation rounds of UE to update its local model and global communication rounds to update the FL global model. We then employ the proposed FL algorithm in wireless networks as a resource allocation optimization problem that captures the trade-off between the FL convergence wall clock time and energy consumption of UEs with heterogeneous computing and power resources. Even though the wireless resource allocation problem of FL is non-convex, we exploit this problems structure to decompose it into three sub-problems and analyze their closed-form solutions as well as insights to problem design. Finally, we illustrate the theoretical analysis for the new algorithm with Tensorflow experiments and extensive numerical results for the wireless resource allocation sub-problems. The experiment results not only verify the theoretical convergence but also show that our proposed algorithm outperforms the vanilla FedAvg algorithm in terms of convergence rate and testing accuracy.
Machine learning (ML) tasks are becoming ubiquitous in todays network applications. Federated learning has emerged recently as a technique for training ML models at the network edge by leveraging processing capabilities across the nodes that collect the data. There are several challenges with employing conventional federated learning in contemporary networks, due to the significant heterogeneity in compute and communication capabilities that exist across devices. To address this, we advocate a new learning paradigm called fog learning which will intelligently distribute ML model training across the continuum of nodes from edge devices to cloud servers. Fog learning enhances federated learning along three major dimensions: network, heterogeneity, and proximity. It considers a multi-layer hybrid learning framework consisting of heterogeneous devices with various proximities. It accounts for the topology structures of the local networks among the heterogeneous nodes at each network layer, orchestrating them for collaborative/cooperative learning through device-to-device (D2D) communications. This migrates from star network topologies used for parameter transfers in federated learning to more distributed topologies at scale. We discuss several open research directions to realizing fog learning.
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