Epidemic forecasting is vital for public health, yet privacy concerns impede inter-institutional data sharing and limit model performance. Federated learning has emerged as a promising approach, but previous research has been limited to specific datasets and temporal prediction. In this paper, we present a privacy-preserving framework, federated framework for epidemic on networks (FFEN), for node-level epidemic prediction on networks that leverages federated learning (FL) to model the spatio-temporal propagation of epidemic severity across data-isolated subnetworks. A Spatio-Temporal Graph Attention Network (STGAT) is proposed to enhance federated epidemic prediction by effectively capturing spatio-temporal dependencies. Extensive simulations on various epidemic processes within a real-world airline network comprehensively evaluate FL’s efficacy under diverse scenarios. To further assess robustness, we introduce the efficacy energy metric, systematically analyzing key factors affecting FL performance. Numerical results validate the effectiveness of FFEN in complex epidemic prediction and demonstrate that STGAT outperforms traditional temporal approaches in capturing dynamic epidemic propagation.

Multicast serves as an important approach for one-to-many communication in data center networks. To reduce overhead and improve scalability, bloom filters are employed in current multicast approaches to store forwarding ports of switches. However, the well-known false positive issue of bloom filter incurs wrong forwarding behaviors and redundant traffic in multicast tree, degrading transmission efficiency and increasing the risk of data leakage. Inspired by the fact that, given the same false positive ratio, the switch in the upper layers of multicast tree generates more redundant traffic, we propose RSBF, a fine-grained and resource-aware multicast approach using skewed bloom filters. Specifically, RSBF maintains multiple bloom filters corresponding to different layers of multicast tree, and allocates more ample space to the bloom filter of the upper layer switches, thereby reducing the overall redundant traffic. The test results of large-scale simulation demonstrate that RSBF reduces both redundant traffic and header overhead by up to 64% and 49% compared with the state-of-the-art approaches, respectively.

Multicast is widely used to support a huge range of applications with one-to-many or many-to-many communication patterns. However, multicast systems do not scale due to considerable state and communication overheads. Some stateful multicast approaches require maintaining the state of each multicast session at switches, thus incurring large memory overhead. Some stateless ones utilize Bloom filter (BF) to encode multicast tree into the packet header to minimize communication overhead, but potentially suffer from the substantial false positive due to the probabilistic nature of Bloom filter. In this paper, we propose a stateless multicast scheme MEB, which uses Bloom filter to achieve large-scale multicast communication with low error, small overhead and high scalability. Specifically, to control the rate of false positive, MEB elaborately selects the hash functions for Bloom filters when constructing the packet header at the sender side, and makes forwarding decision according to packet header at the switch with negligible overhead. We compare MEB against the state-of-the-art multicast system in large-scale simulations. The test results show that MEB reduces the traffic overhead by up to 70% with small error rate.

A technique based on the Extremal Optimization approach is developed to solve joint routing and link scheduling problems in Industrial Wireless Networks. Numerical results show that this technique provides a suitable solution with fast computation capacity.