12 citations · 19 across the 6 of their papers we have counts for
6 papers
XAI-Driven Client Selection for Federated Learning in Scalable 6G Network Slicing
Martino Chiarani, Swastika Roy, Christos Verikoukis +1
In recent years, network slicing has embraced artificial intelligence (AI) models to manage the growing complexity of communication networks. In such a situation, AI-driven zero-to…
Federated Machine Reasoning for Resource Provisioning in 6G O-RAN
Swastika Roy, Hatim Chergui, Adlen Ksentini +1
O-RAN specifications reshape RANs with function disaggregation and open interfaces, driven by RAN Intelligent Controllers. This enables data-driven management through AI/ML but pos…
Joint Explainability and Sensitivity-Aware Federated Deep Learning for Transparent 6G RAN Slicing
Swastika Roy, Farhad Rezazadeh, Hatim Chergui +1
In recent years, wireless networks are evolving complex, which upsurges the use of zero-touch artificial intelligence (AI)-driven network automation within the telecommunication in…
Towards Bridging the FL Performance-Explainability Trade-Off: A Trustworthy 6G RAN Slicing Use-Case
Swastika Roy, Hatim Chergui, Christos Verikoukis
In the context of sixth-generation (6G) networks, where diverse network slices coexist, the adoption of AI-driven zero-touch management and orchestration (MANO) becomes crucial. Ho…
Explanation-Guided Fair Federated Learning for Transparent 6G RAN Slicing
Swastika Roy, Hatim Chergui, Christos Verikoukis
Future zero-touch artificial intelligence (AI)-driven 6G network automation requires building trust in the AI black boxes via explainable artificial intelligence (XAI), where it is…
TEFL: Turbo Explainable Federated Learning for 6G Trustworthy Zero-Touch Network Slicing
Swastika Roy, Hatim Chergui, Christos Verikoukis
Sixth-generation (6G) networks anticipate intelligently supporting a massive number of coexisting and heterogeneous slices associated with various vertical use cases. Such a contex…