Risk-Aware Accelerated Wireless Federated Learning with Heterogeneous Clients
… The authors in [2] introduced an aggregation algorithm, denoted as Federated Average (…
model adaptable to shifting trends. However, applying these algorithms to wireless networks is …
model adaptable to shifting trends. However, applying these algorithms to wireless networks is …
MR-FFL: A Stratified Community-Based Mutual Reliability Framework for Fairness-Aware Federated Learning in Heterogeneous UAV Networks
Fairness-aware federated learning (FFL) plays a crucial role … proposed, concrete to UAV
networks, the majority of existing … for FFL in heterogeneous UAV networks. We first divide UAV …
networks, the majority of existing … for FFL in heterogeneous UAV networks. We first divide UAV …
EEFED: Personalized federated learning of execution&evaluation dual network for CPS intrusion detection
… a novel federated Execution & Evaluation dual network … undermining the original purpose of
Federated Learning. Thus, a … the negative influence of federated learning in imbalanced and …
Federated Learning. Thus, a … the negative influence of federated learning in imbalanced and …
Fog-supported low-latency monitoring of system disruptions in industry 4.0: A federated learning approach
… Therefore, from these results, we can deduce that federated learning can reach the same
performance while improving the resources’ privacy and network overhead, since it avoids to …
performance while improving the resources’ privacy and network overhead, since it avoids to …
A survey on security and privacy of federated learning
… We conclude the paper with much needed future research directions to make FL adaptable
… In this classification, we cover the FL implementation network topology used to build FL …
… In this classification, we cover the FL implementation network topology used to build FL …
Federated learning from pre-trained models: A contrastive learning approach
… performance due to their generality and adaptability to different tasks [57, 58… federated
learning frameworks, we propose a lightweight framework that leverages multiple neural networks …
learning frameworks, we propose a lightweight framework that leverages multiple neural networks …
Fedmp: Federated learning through adaptive model pruning in heterogeneous edge computing
… In this section, we discuss the adaptability and potential extensions of FedMP. We can
extend FedMP to accommodate diverse neural networks by easily replacing different pruning …
extend FedMP to accommodate diverse neural networks by easily replacing different pruning …
Cybertwin-driven federated learning based personalized service provision for 6G-V2X
… networking [24], [25]. The primary contributions of the paper are listed below: 1) The Federated
Learning … provides unsubstantiated and adaptable communication between the AVs and …
Learning … provides unsubstantiated and adaptable communication between the AVs and …
Autofl: Enabling heterogeneity-aware energy efficient federated learning
YG Kim, CJ Wu - MICRO-54: 54th Annual IEEE/ACM International …, 2021 - dl.acm.org
… We also model per-device network instability with SNetwork to represent the network
bandwidth of the respective wireless network (eg, Wi-Fi, LTE, and 5G). In addition, data …
bandwidth of the respective wireless network (eg, Wi-Fi, LTE, and 5G). In addition, data …
Genetic CFL: Hyperparameter optimization in clustered federated learning
… In every communication round, there is a feasibility risk in terms of limited network … the
adaptability of FL in realistic environments. Hyperparameters such as batch size and learning rate …
adaptability of FL in realistic environments. Hyperparameters such as batch size and learning rate …