53 research outputs found
Scalable and Low-Latency Federated Learning with Cooperative Mobile Edge Networking
Federated learning (FL) enables collaborative model training without
centralizing data. However, the traditional FL framework is cloud-based and
suffers from high communication latency. On the other hand, the edge-based FL
framework that relies on an edge server co-located with mobile base station for
model aggregation has low communication latency but suffers from degraded model
accuracy due to the limited coverage of edge server. In light of high accuracy
but high-latency cloud-based FL and low-latency but low-accuracy edge-based FL,
this paper proposes a new FL framework based on cooperative mobile edge
networking called cooperative federated edge learning (CFEL) to enable both
high-accuracy and low-latency distributed intelligence at mobile edge networks.
Considering the unique two-tier network architecture of CFEL, a novel federated
optimization method dubbed cooperative edge-based federated averaging
(CE-FedAvg) is further developed, wherein each edge server both coordinates
collaborative model training among the devices within its own coverage and
cooperates with other edge servers to learn a shared global model through
decentralized consensus. Experimental results based on benchmark datasets show
that CFEL can largely reduce the training time to achieve a target model
accuracy compared with prior FL frameworks.Comment: accepted for publication in IEEE Transactions on Mobile Computin
Towards a Model for Inclusive Healthcare Access post COVID-19
The devastation caused by the COVID-19 pandemic has exposed years of cyclic inequalities faced by disadvantaged and minority communities. Unequal access to healthcare and a lack of financial resources further exacerbates their suffering, especially during a pandemic. In such critical conditions, information technology-based healthcare services can be an efficient way of increasing access to healthcare for these communities. In this paper, we put forward a decision model for guiding the distribution of IT-based healthcare services for racial minorities. We augment the Health Belief Model by adding financial and technology beliefs. We posit that financial inclusion of minority populations increases their ability to access technology and, by extension, IT-based healthcare services. Financial inclusion and the use of secure private technologies like federated learning can indeed enable greater access to healthcare services for minorities. Therefore, we incorporate financial, health, and technology tools to develop a model for equitable delivery of healthcare services and test its applicability in different use-case scenarios
Efficient, Effective, and Realistic Website Fingerprinting Mitigation
Website fingerprinting attacks have been shown to be able to predict the website visited even if the network connection is encrypted and anonymized. These attacks have achieved accuracies as high as 92%. Mitigations to these attacks are using cover/decoy network traffic to add noise, padding to ensure all the network packets are the same size, and introducing network delays to confuse an adversary. Although these mitigations have been shown to be effective, reducing the accuracy to 10%, the overhead is high. The latency overhead is above 100% and the bandwidth overhead is at least 30%. We introduce a new realistic cover traffic algorithm, based on a user’s previous network traffic, to mitigate website fingerprinting attacks. In simulations, our algorithm reduces the accuracy of attacks to 14% with zero latency overhead and about 20% bandwidth overhead. In real-world experiments, our algorithms reduces the accuracy of attacks to 16% with only 20% bandwidth overhead
- …