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Quantum machine learning (QML) has emerged as a promising field that leans on the developments in quantum computing to explore large complex machine learning problems. Recently, some purely quantum machine learning models were proposed such…
Federated Learning (FL) is a collaborative method for training machine learning models while preserving the confidentiality of the participants' training data. Nevertheless, FL is vulnerable to reconstruction attacks that exploit shared…
With increasing concerns for data privacy and ownership, recent years have witnessed a paradigm shift in machine learning (ML). An emerging paradigm, federated learning (FL), has gained great attention and has become a novel design for…
Federated edge learning (FEL) is a promising paradigm of distributed machine learning that can preserve data privacy while training the global model collaboratively. However, FEL is still facing model confidentiality issues due to…
We might hope that when faced with unexpected inputs, well-designed software systems would fire off warnings. Machine learning (ML) systems, however, which depend strongly on properties of their inputs (e.g. the i.i.d. assumption), tend to…
Large language models (LLMs) have recently shown promise in recommendation by providing rich semantic knowledge. While most existing approaches rely on external textual corpora to align LLMs with recommender systems, we revisit a more…
Collaborative Metric Learning (CML) recently emerged as a powerful paradigm for recommendation based on implicit feedback collaborative filtering. However, standard CML methods learn fixed user and item representations, which fails to…
Machine learning (ML) models often require large amounts of data to perform well. When the available data is limited, model trainers may need to acquire more data from external sources. Often, useful data is held by private entities who are…
Machine learning (ML) techniques are increasingly applied to decision-making and control problems in Cyber-Physical Systems among which many are safety-critical, e.g., chemical plants, robotics, autonomous vehicles. Despite the significant…
Federated learning is a promising privacy-preserving paradigm for distributed machine learning. In this context, there is sometimes a need for a specialized process called machine unlearning, which is required when the effect of some…
Knowledge Components (KCs) are foundational to adaptive learning systems, but their manual identification by domain experts is a significant bottleneck. While Large Language Models (LLMs) offer a promising avenue for automating this…
Many of the machine learning (ML) tasks are focused on centralized learning (CL), which requires the transmission of local datasets from the clients to a parameter server (PS) leading to a huge communication overhead. Federated learning…
Social interactions among classroom peers, represented as social learning networks (SLNs), play a crucial role in enhancing learning outcomes. While SLN analysis has recently garnered attention, most existing approaches rely on centralized…
Federated Learning (FL) is a novel machine learning approach that allows the model trainer to access more data samples, by training the model across multiple decentralized data sources, while data access constraints are in place. Such…
Federated learning (FL) offers a privacy-centric distributed learning framework, enabling model training on individual clients and central aggregation without necessitating data exchange. Nonetheless, FL implementations often suffer from…
State-of-the-art models for joint entity recognition and relation extraction strongly rely on external natural language processing (NLP) tools such as POS (part-of-speech) taggers and dependency parsers. Thus, the performance of such joint…
While developing artificial intelligence (AI)-based algorithms to solve problems, the amount of data plays a pivotal role - large amount of data helps the researchers and engineers to develop robust AI algorithms. In the case of building…
Restrictive rules for data sharing in many industries have led to the development of federated learning. Federated learning is a machine-learning technique that allows distributed clients to train models collaboratively without the need to…
Quantum Federated Learning (QFL) enables collaborative training of a Quantum Machine Learning (QML) model among multiple clients possessing quantum computing capabilities, without the need to share their respective local data. However, the…
Federated learning (FL) with a single global server framework is currently a popular approach for training machine learning models on decentralized environment, such as mobile devices and edge devices. However, the centralized server…