Quantum Key Distribution (QKD) is considered to be the ultimate solution to communication security. However, current QKD devices, especially quantum photon sources, are expensive, and they can generate secret keys only at a low rate. In this paper, we first consider homogeneous trusted-relay-based QKD networks where every request has the same amount of secret key requirement and every photon source has the same key distribution rate, and design an approach named RPSP to not only minimize the number of photon sources needed in a network to ensure at least one feasible relay path exists for any potential QKD requests but also save the time to complete a batch of QKD requests by jointly optimizing the routing of relay paths and the provisioning of photon sources to distribute secret keys. Then, we extend RPSP to RPSP-HN which can be applied to heterogeneous networks where requests have different secret key requirements and photon sources distribute keys at different rates. Furthermore, we also extend RPSP to RPSP-HY, which considers that some of the nodes in a network is untrusted. Compared with existing works, RPSP and its extensions focus on more practical scenarios where only some of the nodes are equipped with photon sources and they leverage optical switching to enable dynamic photon source provisioning such that we can utilize QKD devices more efficiently. Extensive simulations show that compared with baseline schemes, RPSP, RPSP-HN, and RPSP-HY can save up to 33%, 37%, and 25% of the time to complete a batch of QKD requests in homogeneous, heterogeneous, and hybrid QKD networks, respectively.

Quantum Key Distribution (QKD) is considered to be an ultimate solution to communication security. However, current QKD devices, especially quantum photon sources, are expensive, and they can generate secret keys only at a low rate. In this paper, we design a system named RPSP for trusted relay-based QKD networks to not only minimize the number of photon sources needed in a network to ensure at least one feasible relay path exists for any potential QKD requests but also save the time to complete a batch of end-to-end QKD requests by jointly optimizing the routing of relay paths and the provisioning of photon sources along each relay path. Compared with existing works, RPSP focuses on a more practical scenario where only some of the nodes are equipped with photon sources and it leverages optical switching to enable dynamic photon source provisioning such that we can utilize such QKD devices in a more efficient way. Extensive simulations show that compared with baseline schemes, RPSP can save up to 87% of the photon sources needed in a trusted relay based QKD network, and 36% of the time to complete a batch of QKD requests.

Congestion Control (CC) plays a vital role in deploying lossless datacenter networks based on Remote Direct Memory Access (RDMA). A high-performance CC scheme should provide low-latency and precise feedback to congestion events. However, no existing CC schemes achieved both features simultaneously. In this paper, we propose LHCC, a Low-latency and Hi-precision Congestion Control scheme for RDMA datacenter networks. LHCC uses out-band signaling to notify the network status and hence a packet sender can detect congestion events within an RTT. In addition, LHCC adjusts packet sending rate by taking into consideration all queues along the entire path that a packet has gone through. Accordingly, it provides a more precise CC compared with existing schemes especially when there are multiple bottlenecks in the networks. We build the LHCC prototype on a real testbed carrying NVIDIA BlueField-3 NICs and AGM39D FPGAs. Both testbed experiments and extensive simulations show that LHCC can reduce the Flow Completion Time (FCT) slow down and reduce the buffer usage (i.e., reduce the queue lengths) by up to 62.5% and 58%, respectively, compared with the state-of-the-art high-precision CC scheme, HPCC.

With billions of IoT devices producing vast data globally, privacy and efficiency challenges arise in AI applications. Federated learning (FL) has been widely adopted to train deep neural networks (DNNs) without privacy leakage. Existing centralized and decentralized FL architectures have limitations, including memory burden, huge bandwidth pressure and non-IID data issues. This paper introduces a novel hybrid FL framework, named FedCD, merging the benefits of both centralized and decentralized FL architectures. FedCD strategically distributes the model based on layer sizes and consensus distances (i.e., the deviation between the local models and the global average models), effectively relieving network bandwidth pressures and accelerating training speed even under the non-IID setting. This method significantly mitigates resource constraints and improves model accuracy, offering a promising solution to the challenges in distributed machine learning. Extensive experiment results show the high effectiveness of FedCD. The total completion time of FedCD is reduced by 16.3%-53% and the average accuracy improvement is 1.85% compared to the baselines.