465 research outputs found
VeryFL: A Verify Federated Learning Framework Embedded with Blockchain
Blockchain-empowered federated learning (FL) has provoked extensive research
recently. Various blockchain-based federated learning algorithm, architecture
and mechanism have been designed to solve issues like single point failure and
data falsification brought by centralized FL paradigm. Moreover, it is easier
to allocate incentives to nodes with the help of the blockchain. Various
centralized federated learning frameworks like FedML, have emerged in the
community to help boost the research on FL. However, decentralized
blockchain-based federated learning framework is still missing, which cause
inconvenience for researcher to reproduce or verify the algorithm performance
based on blockchain. Inspired by the above issues, we have designed and
developed a blockchain-based federated learning framework by embedding Ethereum
network. This report will present the overall structure of this framework,
which proposes a code practice paradigm for the combination of FL with
blockchain and, at the same time, compatible with normal FL training task. In
addition to implement some blockchain federated learning algorithms on smart
contract to help execute a FL training, we also propose a model ownership
authentication architecture based on blockchain and model watermarking to
protect the intellectual property rights of models. These mechanism on
blockchain shows an underlying support of blockchain for federated learning to
provide a verifiable training, aggregation and incentive distribution procedure
and thus we named this framework VeryFL (A Verify Federated Learninig Framework
Embedded with Blockchain). The source code is avaliable on
https://github.com/GTMLLab/VeryFL
Valley-Hall photonic topological insulators with dual-band kink states
Extensive researches have revealed that valley, a binary degree of freedom
(DOF), can be an excellent candidate of information carrier. Recently, valley
DOF has been introduced into photonic systems, and several valley-Hall photonic
topological insulators (PTIs) have been experimentally demonstrated. However,
in the previous valley-Hall PTIs, topological kink states only work at a single
frequency band, which limits potential applications in multiband waveguides,
filters, communications, and so on. To overcome this challenge, here we
experimentally demonstrate a valley-Hall PTI, where the topological kink states
exist at two separated frequency bands, in a microwave substrate-integrated
circuitry. Both the simulated and experimental results demonstrate the
dual-band valley-Hall topological kink states are robust against the sharp
bends of the internal domain wall with negligible inter-valley scattering. Our
work may pave the way for multi-channel substrate-integrated photonic devices
with high efficiency and high capacity for information communications and
processing
Tokenized Model: A Blockchain-Empowered Decentralized Model Ownership Verification Platform
With the development of practical deep learning models like generative AI,
their excellent performance has brought huge economic value. For instance,
ChatGPT has attracted more than 100 million users in three months. Since the
model training requires a lot of data and computing power, a well-performing
deep learning model is behind a huge effort and cost. Facing various model
attacks, unauthorized use and abuse from the network that threaten the
interests of model owners, in addition to considering legal and other
administrative measures, it is equally important to protect the model's
copyright from the technical means. By using the model watermarking technology,
we point out the possibility of building a unified platform for model ownership
verification. Given the application history of blockchain in copyright
verification and the drawbacks of a centralized third-party, this paper
considers combining model watermarking technology and blockchain to build a
unified model copyright protection platform. By a new solution we called
Tokenized Model, it protects the model's copyright by reliable ownership record
and verification mechanism. It also promotes the financial value of model by
constructing the model's transaction process and contribution shares of a
model. In the typical case study, we also study the various performance under
usual scenario to verify the effectiveness of this platform
Realization of a three-dimensional photonic topological insulator
Confining photons in a finite volume is in high demand in modern photonic
devices. This motivated decades ago the invention of photonic crystals,
featured with a photonic bandgap forbidding light propagation in all
directions. Recently, inspired by the discoveries of topological insulators
(TIs), the confinement of photons with topological protection has been
demonstrated in two-dimensional (2D) photonic structures known as photonic TIs,
with promising applications in topological lasers and robust optical delay
lines. However, a fully three-dimensional (3D) topological photonic bandgap has
never before been achieved. Here, we experimentally demonstrate a 3D photonic
TI with an extremely wide (> 25% bandwidth) 3D topological bandgap. The sample
consists of split-ring resonators (SRRs) with strong magneto-electric coupling
and behaves as a 'weak TI', or a stack of 2D quantum spin Hall insulators.
Using direct field measurements, we map out both the gapped bulk bandstructure
and the Dirac-like dispersion of the photonic surface states, and demonstrate
robust photonic propagation along a non-planar surface. Our work extends the
family of 3D TIs from fermions to bosons and paves the way for applications in
topological photonic cavities, circuits, and lasers in 3D geometries
- …