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Federated learning has been extensively studied and is the prevalent method for privacy-preserving distributed learning in edge devices. Correspondingly, continual learning is an emerging field targeted towards learning multiple tasks…

As edge and fog computing become central to modern distributed systems, there's growing interest in combining serverless architectures with privacy-preserving machine learning techniques like federated learning (FL). However, current…

Federated Learning (FL) is a distributed machine learning paradigm designed for privacy-sensitive applications that run on resource-constrained devices with non-Identically and Independently Distributed (IID) data. Traditional FL frameworks…

Collecting relevant and high-quality data is integral to the development of effective Software Vulnerability (SV) prediction models. Most of the current SV datasets rely on SV-fixing commits to extract vulnerable functions and lines.…

Learning-to-Defer (L2D) methods route each query either to a predictive model or to external experts. While existing work studies this problem in batch settings, real-world deployments require handling streaming data, changing expert…

With the development of federated learning (FL), mobile devices (MDs) are able to train their local models with private data and sends them to a central server for aggregation, thereby preventing sensitive raw data leakage. In this paper,…

Network function Virtualization (NFV) and Mobile Edge Computing (MEC) are promising 5G technologies to support resource-demanding mobile applications. In NFV, one must process the service function chain (SFC) in which a set of network…

The vast data deluge at the network's edge is raising multiple challenges for the edge computing community. One of them is identifying edge storage servers where data from edge devices/sensors have to be stored to ensure low latency access…

Real-time machine learning has recently attracted significant interest due to its potential to support instantaneous learning, adaptation, and decision making in a wide range of application domains, including self-driving vehicles,…

Network function virtualization (NFV) enables on-demand network function (NF) deployment providing agile and dynamic network services. Through an evaluation metric that quantifies the minimal reliability among all NFs for all demands,…

On-device training is an emerging approach in machine learning where models are trained on edge devices, aiming to enhance privacy protection and real-time performance. However, edge devices typically possess restricted computational power…

In NFV networks, service functions (SFs) can be deployed on virtual machines (VMs) across multiple domains and then form a service function chain (MSFC) for end-to-end network service provision. However, any software component in a VM-based…

Federated learning is a prominent framework that enables clients (e.g., mobile devices or organizations) to train a collaboratively global model under a central server's orchestration while keeping local training datasets' privacy. However,…

Deploying Transformer-based large language models (LLMs) on resource-constrained edge devices for long-sequence tasks remains challenging due to the quadratic time complexity of self-attention and growing Key-Value (KV) cache demands. While…

Migration and replication of virtual network functions (VNFs) are well-known mechanisms to face dynamic resource requests in Internet Service Provider (ISP) edge networks. They are not only used to reallocate resources in carrier networks,…

Contextual bandit algorithms have been recently studied under the federated learning setting to satisfy the demand of keeping data decentralized and pushing the learning of bandit models to the client side. But limited by the required…

Network Function Virtualization (NFV) and Software Distributed Networking (SDN) technologies play a crucial role in enabling 5G system and beyond. A synergy between these both technologies has been identified for enabling a new concept…

Deploying services efficiently while satisfying their quality requirements is a major challenge in network slicing. Effective solutions place instances of the services' virtual network functions (VNFs) at different locations of the cellular…

Resiliency plays a critical role in designing future communication networks. How to make edge computing systems resilient against unpredictable failures and fluctuating demand is an important and challenging problem. To this end, this paper…

Privacy concerns in machine learning are heightened by regulations such as the GDPR, which enforces the "right to be forgotten" (RTBF), driving the emergence of machine unlearning as a critical research field. Vertical Federated Learning…