2 research outputs found
Resilience Bounds of Sensing-Based Network Clock Synchronization
Recent studies exploited external periodic synchronous signals to synchronize
a pair of network nodes to address a threat of delaying the communications
between the nodes. However, the sensing-based synchronization may yield faults
due to nonmalicious signal and sensor noises. This paper considers a system of
N nodes that will fuse their peer-to-peer synchronization results to correct
the faults. Our analysis gives the lower bound of the number of faults that the
system can tolerate when N is up to 12. If the number of faults is no greater
than the lower bound, the faults can be identified and corrected. We also prove
that the system cannot tolerate more than N-2 faults. Our results can guide the
design of resilient sensing-based clock synchronization systems
Resilience Bounds of Network Clock Synchronization with Fault Correction
The Internet of Things (IoT) will be a main data generation infrastructure
for achieving better system intelligence. This paper considers the design and
implementation of a practical privacy-preserving collaborative learning scheme,
in which a curious learning coordinator trains a better machine learning model
based on the data samples contributed by a number of IoT objects, while the
confidentiality of the raw forms of the training data is protected against the
coordinator. Existing distributed machine learning and data encryption
approaches incur significant computation and communication overhead, rendering
them ill-suited for resource-constrained IoT objects. We study an approach that
applies independent random projection at each IoT object to obfuscate data and
trains a deep neural network at the coordinator based on the projected data
from the IoT objects. This approach introduces light computation overhead to
the IoT objects and moves most workload to the coordinator that can have
sufficient computing resources. Although the independent projections performed
by the IoT objects address the potential collusion between the curious
coordinator and some compromised IoT objects, they significantly increase the
complexity of the projected data. In this paper, we leverage the superior
learning capability of deep learning in capturing sophisticated patterns to
maintain good learning performance. Extensive comparative evaluation shows that
this approach outperforms other lightweight approaches that apply additive
noisification for differential privacy and/or support vector machines for
learning in the applications with light to moderate data pattern complexities