Visual Person Understanding Through Multi-task and Multi-dataset Learning. - Kilian Pfeiffer; Alexander Hermans; István Sárándi; Mark Weber; Bastian Leibe (GCPR, 2019)

DISTREAL: Distributed Resource-Aware Learning in Heterogeneous Systems. - Martin Rapp; Ramin Khalili; Kilian Pfeiffer; Jörg Henkel (AAAI, 2022)

CoCo-FL: Communication- and Computation-Aware Federated Learning via Partial NN Freezing and Quantization. - Kilian Pfeiffer; Martin Rapp; Ramin Khalili; Jörg Henkel (CoRR, 2022)

Aggregating Capacity in FL through Successive Layer Training for Computationally-Constrained Devices. - Kilian Pfeiffer; Ramin Khalili; Jörg Henkel (NeurIPS, 2023)

Federated Learning for Computationally Constrained Heterogeneous Devices: A Survey. - Kilian Pfeiffer; Martin Rapp; Ramin Khalili; Jörg Henkel (ACM Comput. Surv., 2023)

CoCoFL: Communication- and Computation-Aware Federated Learning via Partial NN Freezing and Quantization. - Kilian Pfeiffer; Martin Rapp; Ramin Khalili; Jörg Henkel (Trans. Mach. Learn. Res., 2023)

Energy-Aware Heterogeneous Federated Learning via Approximate Systolic DNN Accelerators. - Kilian Pfeiffer; Konstantinos Balaskas; Kostas Siozios; Jörg Henkel (CoRR, 2024)

Efficient Federated Finetuning of Tiny Transformers with Resource-Constrained Devices. - Kilian Pfeiffer; Mohamed Aboelenien Ahmed; Ramin Khalili; Jörg Henkel (CoRR, 2024)

Efficient Zero-Order Federated Finetuning of Language Models for Resource-Constrained Devices. - Mohamed Aboelenien Ahmed; Kilian Pfeiffer; Ramin Khalili; Heba Khdr; Jörg Henkel (CoRR, 2025)

Accelerated Training on Low-Power Edge Devices. - Mohamed Aboelenien Ahmed; Kilian Pfeiffer; Heba Khdr; Osama Abboud; Ramin Khalili; Jörg Henkel (CoRR, 2025)

Energy-Aware Heterogeneous Federated Learning via Approximate DNN Accelerators. - Kilian Pfeiffer; Konstantinos Balaskas; Kostas Siozios; Jörg Henkel (IEEE Trans. Comput. Aided Des. Integr. Circuits Syst., 2025)