171 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
PKCδ is required for porcine reproductive and respiratory syndrome virus replication
AbstractProtein kinase C (PKC) that transduces signals to modulate a wide range of cellular functions has been shown to regulate a number of viral infections. Herein, we showed that inhibition of PKC with the PKC inhibitor GF109203X significantly impaired porcine reproductive and respiratory syndrome virus (PRRSV) replication. Inhibition of PKC led to virus yield reduction, which was associated with decreased viral RNA synthesis and lowered virus protein expression. And this inhibitory effect by PKC inhibitor was shown to occur at the early stage of PRRSV infection. Subsequently, we found that PRRSV infection activated PKCδ in PAMs and knockdown of PKCδ by small interfering RNA (siRNA) suppressed PRRSV replication, suggesting that novel PKCδ may play an important factor in PRRSV replication. Taken together, these data imply that PKC is involved in PRRSV infection and beneficial to PRRSV replication, extending our understanding of PRRSV replication
Modeling Multi-aspect Preferences and Intents for Multi-behavioral Sequential Recommendation
Multi-behavioral sequential recommendation has recently attracted increasing
attention. However, existing methods suffer from two major limitations.
Firstly, user preferences and intents can be described in fine-grained detail
from multiple perspectives; yet, these methods fail to capture their
multi-aspect nature. Secondly, user behaviors may contain noises, and most
existing methods could not effectively deal with noises. In this paper, we
present an attentive recurrent model with multiple projections to capture
Multi-Aspect preferences and INTents (MAINT in short). To extract multi-aspect
preferences from target behaviors, we propose a multi-aspect projection
mechanism for generating multiple preference representations from multiple
aspects. To extract multi-aspect intents from multi-typed behaviors, we propose
a behavior-enhanced LSTM and a multi-aspect refinement attention mechanism. The
attention mechanism can filter out noises and generate multiple intent
representations from different aspects. To adaptively fuse user preferences and
intents, we propose a multi-aspect gated fusion mechanism. Extensive
experiments conducted on real-world datasets have demonstrated the
effectiveness of our model
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