activity
20172026
most citedLow-latency Federated Learning and Blockchain for Edge Association in Digital Twin empowered 6G Networks

495 citations · 548 across the 20 of their papers we have counts for

collaborators

22 papers

cs.NI2026

Token Communications (TokCom): A Unified AI-Native Communication Framework

Yaru Fu, Liang Ji, Sabita Maharjan +1

As artificial intelligence (AI) evolves from static perception to generative reasoning and autonomous agency, the fundamental principles of wireless communications are undergoing a…

cs.CR2026

X-NegoBox: An Explainable Privacy-Budget Negotiation Framework for Secure Peer-to-Peer Energy Data Exchange

Poushali Sengupta, Sabita Maharjan, Frank Eliassen +1

The decentralization of modern energy systems is transforming consumers into prosumers who continuously exchange data with aggregators, peers, and market operators. While such data…

cs.LG2026

Reliable Explanations or Random Noise? A Reliability Metric for XAI

Poushali Sengupta, Sabita Maharjan, Frank Eliassen +2

In recent years, explaining decisions made by complex machine learning models has become essential in high-stakes domains such as energy systems, healthcare, finance, and autonomou…

cs.CL2026

Context Dependence and Reliability in Autoregressive Language Models

Poushali Sengupta, Shashi Raj Pandey, Sabita Maharjan +1

Large language models (LLMs) generate outputs by utilizing extensive context, which often includes redundant information from prompts, retrieved passages, and interaction history.…

math.OC2026

Adaptive Robust Control for Uncertain Systems with Ellipsoid-Set Learning

Xuehui Ma, Shiliang Zhang, Zhiyong Sun +2

Despite the celebrated success of stochastic control approaches for uncertain systems, such approaches are limited in the ability to handle non-Gaussian uncertainties. This work pr…

cs.LG2026

Explainability of Complex AI Models with Correlation Impact Ratio

Poushali Sengupta, Rabindra Khadka, Sabita Maharjan +5

Complex AI systems make better predictions but often lack transparency, limiting trustworthiness, interpretability, and safe deployment. Common post hoc AI explainers, such as LIME…