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1. Privacy Preserving Personalization for Video Facial Expression Recognition Using Federated Learning

   https://doi.org/10.1145/3536221.3556614  

   Salman, Ali ; Busso, Carlos ( November 2022 , ACM International Conference on Multimodal Interaction (ICMI 2022))

   The increased ubiquitousness of small smart devices, such as cell- phones, tablets, smart watches and laptops, has led to unique user data, which can be locally processed. The sensors (e.g., microphones and webcam) and improved hardware of the new devices have al- lowed running deep learning models that 20 years ago would have been exclusive to high-end expensive machines. In spite of this progress, state-of-the-art algorithms for facial expression recognition (FER) rely on architectures that cannot be implemented on these devices due to computational and memory constraints. Alternatives involving cloud-based solutions impose privacy barriers that prevent their adoption or user acceptance in wide range of applications. This paper proposes a lightweight model that can run in real-time for image facial expression recognition (IFER) and video facial expression recognition (VFER). The approach relies on a personalization mechanism locally implemented for each subject by fine-tuning a central VFER model with unlabeled videos from a target subject. We train the IFER model to generate pseudo labels and we select the videos with the highest confident predictions to be used for adaptation. The adaptation is performed by implementing a federated learning strategy where the weights of the local model are averaged and used bymore »the central VFER model. We demonstrate that this approach can improve not only the performance on the edge device providing personalized models to the users, but also the central VFER model. We implement a federated learning strategy where the weights of the local models are averaged and used by the central VFER. Within corpus and cross-corpus evaluations on two emotional databases demonstrate that edge models adapted with our personalization strategy achieve up to 13.1% gains in F1-scores. Furthermore, the federated learning implementation improves the mean micro F1-score of the central VFER model by up to 3.4%. The proposed lightweight solution is ideal for interactive user interfaces that preserve the data of the users.« less
2. A Fog Robotics Approach to Deep Robot Learning: Application to Object Recognition and Grasp Planning in Surface Decluttering

   Tanwani, Ajay Kumar ; Mor, Nitest ; Kubiatowicz, John ; Gonzalez, Joseph E. ; Goldberg, Ken ( May 2019 , Proceedings - IEEE International Conference on Robotics and Automation)

   The growing demand of industrial, automotive and service robots presents a challenge to the centralized Cloud Robotics model in terms of privacy, security, latency, bandwidth, and reliability. In this paper, we present a ‘Fog Robotics’ approach to deep robot learning that distributes compute, storage and networking resources between the Cloud and the Edge in a federated manner. Deep models are trained on non-private (public) synthetic images in the Cloud; the models are adapted to the private real images of the environment at the Edge within a trusted network and subsequently, deployed as a service for low-latency and secure inference/prediction for other robots in the network. We apply this approach to surface decluttering, where a mobile robot picks and sorts objects from a cluttered floor by learning a deep object recognition and a grasp planning model. Experiments suggest that Fog Robotics can improve performance by sim-to-real domain adaptation in comparison to exclusively using Cloud or Edge resources, while reducing the inference cycle time by 4 to successfully declutter 86% of objects over 213 attempts.
3. MetaGater: Fast Learning of Conditional Channel Gated Networks via Federated Meta-Learning

   https://doi.org/10.1109/MASS52906.2021.00031  

   Lin, Sen ; Yang, Li ; He, Zhezhi ; Fan, Deliang ; Zhang, Junshan ( October 2021 , 2021 IEEE 18th International Conference on Mobile Ad Hoc and Smart Systems (MASS))

   There has recently been an increasing interest in computationally-efficient learning methods for resource-constrained applications, e.g., pruning, quantization and channel gating. In this work, we advocate a holistic approach to jointly train the backbone network and the channel gating which can speed up subnet selection for a new task at the resource-limited node. In particular, we develop a federated meta-learning algorithm to jointly train good meta-initializations for both the backbone networks and gating modules, by leveraging the model similarity across learning tasks on different nodes. In this way, the learnt meta-gating module effectively captures the important filters of a good meta-backbone network, and a task-specific conditional channel gated network can be quickly adapted from the meta-initializations using data samples of the new task. The convergence of the proposed federated meta-learning algorithm is established under mild conditions. Experimental results corroborate the effectiveness of our method in comparison to related work.
4. On High-Dimensional Graph Learning Under Total Positivity

   https://doi.org/10.1109/IEEECONF53345.2021.9723191  

   Tugnait, Jitendra K. ( October 2021 , 22021 55th Asilomar Conference on Signals, Systems, and Computers)

   We consider the problem of estimating the structure of an undirected weighted sparse graphical model of multivariate data under the assumption that the underlying distribution is multivariate totally positive of order 2, or equivalently, all partial correlations are non-negative. Total positivity holds in several applications. The problem of Gaussian graphical model learning has been widely studied without the total positivity assumption where the problem can be formulated as estimation of the sparse precision matrix that encodes conditional dependence between random variables associated with the graph nodes. An approach that imposes total positivity is to assume that the precision matrix obeys the Laplacian constraints which include constraining the off-diagonal elements of the precision matrix to be non-positive. In this paper we investigate modifications to widely used penalized log-likelihood approaches to enforce total positivity but not the Laplacian structure. An alternating direction method of multipliers (ADMM) algorithm is presented for constrained optimization under total positivity and lasso as well as adaptive lasso penalties. Numerical results based on synthetic data show that the proposed constrained adaptive lasso approach significantly outperforms existing Laplacian-based approaches, both statistical and smoothness-based non-statistical.
5. Attack of the Tails: Yes, You Really Can Backdoor Federated Learning

   Wang, Hongyi ; Sreenivasan, Kartik ; Rajput, Shashank ; Vishwakarma, Harit ; Agarwal, Saurabh ; Sohn, Jy-yong ; Lee, Kangwook ; Papailiopoulos, Dimitris ( January 2020 , Advances in neural information processing systems)

   Due to its decentralized nature, Federated Learning (FL) lends itself to adversarial attacks in the form of backdoors during training. The goal of a backdoor is to corrupt the performance of the trained model on specific sub-tasks (e.g., by classifying green cars as frogs). A range of FL backdoor attacks have been introduced in the literature, but also methods to defend against them, and it is currently an open question whether FL systems can be tailored to be robust against backdoors. In this work, we provide evidence to the contrary. We first establish that, in the general case, robustness to backdoors implies model robustness to adversarial examples, a major open problem in itself. Furthermore, detecting the presence of a backdoor in a FL model is unlikely assuming first order oracles or polynomial time. We couple our theoretical results with a new family of backdoor attacks, which we refer to as edge-case backdoors. An edge-case backdoor forces a model to misclassify on seemingly easy inputs that are however unlikely to be part of the training, or test data, i.e., they live on the tail of the input distribution. We explain how these edge-case backdoors can lead to unsavory failures and maymore »have serious repercussions on fairness, and exhibit that with careful tuning at the side of the adversary, one can insert them across a range of machine learning tasks (e.g., image classification, OCR, text prediction, sentiment analysis).« less