The rapid growth of data from edge devices has catalyzed the performance of machine learning algorithms. However, the data generated resides at client devices thus there are majorly two challenge faced by traditional machine learning paradigms - centralization of data for training and secondly for most the generated data the class labels are missing and there is very poor incentives to clients to manually label their data owing to high cost and lack of expertise. To overcome these issues, there have been initial attempts to handle unlabelled data in a privacy preserving distributed manner using unsupervised federated data clustering. The goal is partition the data available on clients into $k$ partitions (called clusters) without actual exchange of data. Most of the existing algorithms are highly dependent on data distribution patterns across clients or are computationally expensive. Furthermore, due to presence of skewed nature of data across clients in most of practical scenarios existing models might result in clients suffering high clustering cost making them reluctant to participate in federated process. To this, we are first to introduce the idea of personalization in federated clustering. The goal is achieve balance between achieving lower clustering cost and at same time achieving uniform cost across clients. We propose p-FClus that addresses these goal in a single round of communication between server and clients. We validate the efficacy of p-FClus against variety of federated datasets showcasing it's data independence nature, applicability to any finite $\ell$-norm, while simultaneously achieving lower cost and variance. 

Fair Federated Data Clustering through Personalization: Bridging the Gap
  between Diverse Data Distributions

Equivocation and ambiguity in public speech are well-studied discourse phenomena, especially in political science and analysis of political interviews. Inspired by the well-grounded theory on equivocation, we aim to resolve the closely related problem of response clarity in questions extracted from political interviews, leveraging the capabilities of Large Language Models (LLMs) and human expertise. To this end, we introduce a novel taxonomy that frames the task of detecting and classifying response clarity and a corresponding clarity classification dataset which consists of question-answer (QA) pairs drawn from political interviews and annotated accordingly. Our proposed two-level taxonomy addresses the clarity of a response in terms of the information provided for a given question (high-level) and also provides a fine-grained taxonomy of evasion techniques that relate to unclear, ambiguous responses (lower-level). We combine ChatGPT and human annotators to collect, validate and annotate discrete QA pairs from political interviews, to be used for our newly introduced response clarity task. We provide a detailed analysis and conduct several experiments with different model architectures, sizes and adaptation methods to gain insights and establish new baselines over the proposed dataset and task. 

pdf/i-never-said-that-a-dataset-taxonomy-and-baselines-on-4447omtbie9i.pdf

"I Never Said That": A dataset, taxonomy and baselines on response
  clarity classification

Machine reading comprehension (MRC) is a challenging task in natural language processing that demonstrates the language understanding of the machine. An approach to tackle this challenge requires the machine to answer the question about the given context when needed and abstain from answering when there is no answer. Recent works attempted to solve this challenge with various comprehensive neural network architectures for sequences such as SAN, U-Net, EQuANt, and others that were trained on the SQuAD 2.0 dataset containing unanswerable questions. However, the robustness of these approaches has not been evaluated. In this paper, we propose a data augmentation approach that converts answerable questions to unanswerable questions in the SQuAD 2.0 dataset by altering the entities in the question to its antonym from ConceptNet which is a semantic network. The augmented data is, then, fitted into the U-Net question answering model to evaluate the robustness of the model.

A Framework for Evaluating MRC Approaches with Unanswerable Questions

The rapid growth of both the Industrial Internet of Things (IIoT) and Artificial Intelligence (AI) results in a high demand for AI applications in devices. To achieve high levels of accuracy, AI applications typically require a large amount of annotated data. Accessing such data is challenging in various applications such as healthcare, finance and information security. Federated learning (FL) is one of the strategies that was proposed to overcome this challenge. Specifically, FL enables the AI model in the centralized system to be trained without any prior knowledge of the information on the devices. Recent FLs have the disadvantage that they are dependent upon a centralized system, and thus are susceptible to single points of failure. This paper proposes a strategy that employs FL in a decentralized environment where devices can communicate with each other to increase the accuracy of the AI model in each device. Furthermore, we evaluate the proposed strategy in the image classification task with the ResNet50 architecture and the CIFAR-10 dataset. The evaluation shows that the ResNet50 model trained in the decentralized environment can achieve comparable results to the model trained in the centralized environment.

Decentralized Federated Learning Strategy with Image Classification using ResNet Architecture

Mining operations are increasingly adopting advanced technologies to improve operations. These technologies include sensors, drones, robotic drills, and autonomous haul trucks and generate large quantities of data. Analyzing these large datasets can significantly improve mining practices by optimizing outcomes. For example, in drill and blast designs, we can optimize through learning from distributed data processing across multiple sites. However, mines are not equipped to process and handle large amounts of data. The advent of Machine Learning (ML) has enabled the effective handling of large volumes of complex data and the extraction of valuable insights. To accelerate the development of machine learning models for drill and blast design development, we propose an architecture based on two emerging fields of machine learning: Federated Learning (FL) and meta-learning. The proposed architecture facilitates collaboration and encourages sharing best practices among mines while ensuring the data remains local to each mine. In this research, we analyze the proposed architecture, detailing its components and functionalities. Specifically, we aim to determine the optimal learning rate and boosting rounds for unseen mine data using meta-learning. Our evaluation demonstrates that our learning rate and boosting rounds significantly decrease the Root Mean Squared Error (RMSE) value over a standard learning rate by 28.6%, showing the efficacy of the proposed architecture. We examine the potential advantages and inherent challenges of developing a robust system with our architecture, setting the groundwork for future research. 

An Integrated Federated Learning and Meta-Learning Approach for Mining Operations

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Papers

A Framework for Evaluating MRC Approaches with Unanswerable Questions

Decentralized Federated Learning Strategy with Image Classification using ResNet Architecture

An Integrated Federated Learning and Meta-Learning Approach for Mining Operations

Safeguarding LLM-Applications: Specify or Train?