Papers
6
Total Citations
231
H-Index
4
About
Sanaz Kianoush is a leading researcher at the intersection of distributed machine learning, wireless communications, and industrial automation. Her work primarily focuses on enabling next-generation autonomous systems—including robots, vehicles, and drones—through communication-efficient, decentralized intelligence. Kianoush’s most impactful contribution is her pioneering exploration of federated learning for industrial networks, as demonstrated in her highly cited 2021 paper (186 citations), which outlines how federated learning can meet the ultra-reliable, low-latency demands of connected, cooperative systems. She further advanced this field by proposing a joint decentralized federated learning and communications framework, addressing the critical need for resource co-design in distributed industrial architectures. Recognizing the environmental cost of these technologies, Kianoush also developed a framework for analyzing the energy and carbon footprint of distributed edge learning, highlighting her commitment to sustainable AI. Her broader research portfolio includes wireless sensing for human motion recognition and sensor fusion for robotized environments, showcasing her versatility in human-machine interaction and Industry 4.0 applications. Through her work, Kianoush is shaping a future where intelligent, autonomous systems are not only faster and more efficient but also environmentally responsible.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3
- 4
- 5Wireless Sensing for Device-Free Recognition of Human Motion2 citations · 2017
- 6