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Found 19 papers, 3 papers with code
Comments on "Privacy-Enhanced Federated Learning Against Poisoning Adversaries"
al., IEEE TIFS'23), several subsequent papers continued to reference Liu et al. (IEEE TIFS'21) as a potential solution for private federated learning.
Attesting Distributional Properties of Training Data for Machine Learning
The success of machine learning (ML) has been accompanied by increased concerns about its trustworthiness.
Orthogonal Sampling based Broad-Band Signal Generation with Low-Bandwidth Electronics
To circumvent the sampling rate and bandwidth problem of electronic DACs, we demonstrate the generation of wide-band signals with low-bandwidth electronics.
WW-FL: Secure and Private Large-Scale Federated Learning
Federated learning (FL) is an efficient approach for large-scale distributed machine learning that promises data privacy by keeping training data on client devices.
ScionFL: Efficient and Robust Secure Quantized Aggregation
In this paper, we unite both research directions by introducing ScionFL, the first secure aggregation framework for FL that operates efficiently on quantized inputs and simultaneously provides robustness against malicious clients.
Reconfigurable and Real-Time Nyquist OTDM Demultiplexing in Silicon Photonics
We demonstrate for the first time, to the best of our knowledge, reconfigurable and real-time orthogonal time-domain demultiplexing of coherent multilevel Nyquist signals in silicon photonics.
Optical Channel Aggregation by Coherent Spectral Superposition with Electro-Optic Modulators
As the bit rates of routed data streams exceed the throughput of single wavelength-division multiplexing channels, spectral traffic aggregation becomes essential for optical network scaling.
BAFFLE: TOWARDS RESOLVING FEDERATED LEARNING’S DILEMMA - THWARTING BACKDOOR AND INFERENCE ATTACKS
Recently, federated learning (FL) has been subject to both security and privacy attacks posing a dilemmatic challenge on the underlying algorithmic designs: On the one hand, FL is shown to be vulnerable to backdoor attacks that stealthily manipulate the global model output using malicious model updates, and on the other hand, FL is shown vulnerable to inference attacks by a malicious aggregator inferring information about clients’ data from their model updates.
Classification of Finite Highly Regular Vertex-Coloured Graphs
A coloured graph is k-ultrahomogeneous if every isomorphism between two induced subgraphs of order at most k extends to an automorphism.
Combinatorics 05C60, 05B20, 05B05, 05C75
Conservative Extensions in Horn Description Logics with Inverse Roles
We investigate the decidability and computational complexity of conservative extensions and the related notions of inseparability and entailment in Horn description logics (DLs) with inverse roles.
Trustworthy AI Inference Systems: An Industry Research View
Additionally, such systems should also use Privacy-Enhancing Technologies (PETs) to protect customers' data at any time.
The agnostic sampling transceiver
Increasing capacity demands in the access networks require inventive concepts for the transmission and distribution of digital as well as analog signals over the same network.
CryptoSPN: Privacy-preserving Sum-Product Network Inference
AI algorithms, and machine learning (ML) techniques in particular, are increasingly important to individuals' lives, but have caused a range of privacy concerns addressed by, e. g., the European GDPR.
Long-term Large-scale Mapping and Localization Using maplab
This paper discusses a large-scale and long-term mapping and localization scenario using the maplab open-source framework.
Chameleon: A Hybrid Secure Computation Framework for Machine Learning Applications
Chameleon departs from the common assumption of additive or linear secret sharing models where three or more parties need to communicate in the online phase: the framework allows two parties with private inputs to communicate in the online phase under the assumption of a third node generating correlated randomness in an offline phase.
maplab: An Open Framework for Research in Visual-inertial Mapping and Localization
On the other hand, maplab provides the research community with a collection of multisession mapping tools that include map merging, visual-inertial batch optimization, and loop closure.
Robotics
Topomap: Topological Mapping and Navigation Based on Visual SLAM Maps
Then, we create a set of convex free-space clusters, which are the vertices of the topological map.
Robotics
A Survey of Qualitative Spatial and Temporal Calculi -- Algebraic and Computational Properties
Qualitative Spatial and Temporal Reasoning (QSTR) is concerned with symbolic knowledge representation, typically over infinite domains.
Algebraic Properties of Qualitative Spatio-Temporal Calculi
Qualitative spatial and temporal reasoning is based on so-called qualitative calculi.
