With increased awareness comes unprecedented expectations. We live in a digital, cloud era wherein the underlying information architectures are expected to be elastic, secure, resilient, and handle petabyte scaling. The expectation of epic proportions from the next generation of the data frameworks is to not only do all of the above but also build it on a foundation of trust and explainability across multi-organization business networks. From cloud providers to automobile industries or even vaccine manufacturers, components are often sourced by a complex, not full digitized thread of disjoint suppliers. Building Machine Learning and AI-based order fulfillment and predictive models, remediating issues, is a challenge for multi-organization supply chain automation. We posit that Federated Learning in conjunction with blockchain and smart contracts are technologies primed to tackle data privacy and centralization challenges. In this paper, motivated by challenges in the industry, we propose a decentralized distributed system in conjunction with a recommendation system model (Matrix Factorization) that is trained using Federated Learning on an Ethereum blockchain network. We leverage smart contracts that allow decentralized serverless aggregation to update local-ized items vectors. Furthermore, we utilize Homomorphic Encryption (HE) to allow sharing the encrypted gradients over the network while maintaining their privacy. Based on our results, we argue that training a model over a serverless Blockchain network using smart contracts will provide the same accuracy as in a centralized model while maintaining our serverless model privacy and reducing the overhead communication to a central server. Finally, we assert such a system that provides transparency, audit-ready and deep insights into supply chain operations for enterprise cloud customers resulting in cost savings and higher Quality of Service (QoS).

In Federated Learning there have been many op-timization methods that allow flexible local updating such as FedAvg that has become the de facto mechanism for averaging local stochastic gradient descent without sharing the data. Classic FL methods such as FedAvg struggle with trust and data leakage issues. In FedAvg and similar techniques, clients assume the aggregator server is a trusted but curious server. However, even if the server is trusted, the models still leak a lot of data through the weights. Several techniques have been proposed to reduce data leakage. One mechanism involves sharing pieces of the data with the server, but it violates the key privacy assumption of federated learning. Other solutions such as Federated Learning with Differential Privacy aim to reduce data leakage by adding noise to the weights/gradients. However, there is a trade-off between accuracy and the amount of noise added.In this paper, we propose a practical Federated Learning algorithm of deep neural networks on iterative model averaging we called FederatedTree. While FedAvg with differential privacy adds noise to the weights to provide a level of privacy, our algorithm applies a secure sequential averaging without adding noise to the models. FederatedTree solves the trust issue between client-to-client, client-to-server (if exists) and reduces the amount of data leakage without adding noise that lowers the model accuracy. The results show that the FederatedTree algorithm provides a high privacy rate with higher accuracy on popular datasets: MNIST, Fashion MNIST, CIFAR-10. Furthermore, FederatedTree utilizes a binary tree structure to reduce the sequential averaging time and remove the overhead of the excessive communication between the server and the clients.

In this paper, we propose a system for large-scale image retrieval on everyday scenes with common objects by leveraging advances in deep learning and natural language processing (NLP). Unlike recent state-of-the-art approaches that extract image features from a convolutional neural network (CNN), our system exploits the predictions made by deep neural networks for image understanding tasks. Our system aims to capture the relationships between objects in an everyday scene rather than just the individual objects in the scene. It works as follows: For each image in the database, it generates most probable captions and detects objects in the image using state-of-the-art deep learning models. The captions are parsed and represented by tree structures using NLP techniques. These are stored and indexed in a database system. When a user poses a query image, its caption is generated using deep learning and parsed into its corresponding tree structures. Then an optimized tree-pattern query is constructed and executed on the database to retrieve a set of candidate images. Finally, these candidate images are ranked using the tree-edit distance metric computed on the tree structures. A query based on only objects detected in the query image can also be formulated and executed. In this case, the ranking scheme uses the probabilities of the detected objects. We evaluated the performance of our system on the Microsoft COCO dataset containing everyday scenes (with common objects) and observed that our system can outperform state-of-the-art techniques in terms of mean average precision for large-scale image retrieval.

We present a system for large-scale image retrieval on everyday scenes with common objects. Our system leverages advances in deep learning and natural language processing (NLP) for improved understanding of images by capturing the relationships between the objects within an image. As a result, a user can retrieve highly relevant images and obtain suggestions for similar image queries to further explore the repository. Each image in the repository is processed (using deep learning) to obtain the most probable captions and objects in it. The captions are parsed into tree structures using NLP techniques, and stored and indexed in a database system. When a query image is posed, an optimized tree-pattern query is executed by the database system to obtain candidate matches, which are then ranked using tree-edit distance of the tree structures to output the top-k matches. Word embeddings and Bloom filters are used to obtain similar image queries. By clicking the suggested similar image queries, a user can intuitively explore the repository.