169 citations · 186 across the 12 of their papers we have counts for
15 papers
Sparse Federated Echo State Networks for Time-Series Classification via Smooth-L1 Regularization
Gengcheng Lyu, Manuel Röder, Frank-Michael Schleif
Echo State Networks (ESNs), as lightweight implementations of reservoir computing, provide efficient solutions for temporal data processing in resource-constrained environments. Wh…
Federated Attack Campaign Detection via Contrastive Encoding of Threat Indicators in Gradient Updates
Manuel Röder, Bibin Babu, Frank-Michael Schleif
Detecting orchestrated cyberattack campaigns that span multiple organizations traditionally requires sharing sensitive telemetry and threat intelligence across institutional bounda…
Domain Borders Are There to Be Crossed With Federated Few-Shot Adaptation
Manuel Röder, Christoph Raab, Frank-Michael Schleif
Federated Learning has emerged as a leading paradigm for decentralized, privacy-preserving learning, particularly relevant in the era of interconnected edge devices equipped with s…
Deep Transfer Hashing for Adaptive Learning on Federated Streaming Data
Manuel Röder, Frank-Michael Schleif
This extended abstract explores the integration of federated learning with deep transfer hashing for distributed prediction tasks, emphasizing resource-efficient client training fr…
Sparse Uncertainty-Informed Sampling from Federated Streaming Data
Manuel Röder, Frank-Michael Schleif
We present a numerically robust, computationally efficient approach for non-I.I.D. data stream sampling in federated client systems, where resources are limited and labeled data fo…
Efficient Cross-Domain Federated Learning by MixStyle Approximation
Manuel Röder, Leon Heller, Maximilian Münch +1
With the advent of interconnected and sensor-equipped edge devices, Federated Learning (FL) has gained significant attention, enabling decentralized learning while maintaining data…