44 research outputs found
Learning to Auto Weight: Entirely Data-driven and Highly Efficient Weighting Framework
Example weighting algorithm is an effective solution to the training bias
problem, however, most previous typical methods are usually limited to human
knowledge and require laborious tuning of hyperparameters. In this paper, we
propose a novel example weighting framework called Learning to Auto Weight
(LAW). The proposed framework finds step-dependent weighting policies
adaptively, and can be jointly trained with target networks without any
assumptions or prior knowledge about the dataset. It consists of three key
components: Stage-based Searching Strategy (3SM) is adopted to shrink the huge
searching space in a complete training process; Duplicate Network Reward (DNR)
gives more accurate supervision by removing randomness during the searching
process; Full Data Update (FDU) further improves the updating efficiency.
Experimental results demonstrate the superiority of weighting policy explored
by LAW over standard training pipeline. Compared with baselines, LAW can find a
better weighting schedule which achieves much more superior accuracy on both
biased CIFAR and ImageNet.Comment: Accepted by AAAI 202
Design of Two-Level Incentive Mechanisms for Hierarchical Federated Learning
Hierarchical Federated Learning (HFL) is a distributed machine learning
paradigm tailored for multi-tiered computation architectures, which supports
massive access of devices' models simultaneously. To enable efficient HFL, it
is crucial to design suitable incentive mechanisms to ensure that devices
actively participate in local training. However, there are few studies on
incentive mechanism design for HFL. In this paper, we design two-level
incentive mechanisms for the HFL with a two-tiered computing structure to
encourage the participation of entities in each tier in the HFL training. In
the lower-level game, we propose a coalition formation game to joint optimize
the edge association and bandwidth allocation problem, and obtain efficient
coalition partitions by the proposed preference rule, which can be proven to be
stable by exact potential game. In the upper-level game, we design the
Stackelberg game algorithm, which not only determines the optimal number of
edge aggregations for edge servers to maximize their utility, but also optimize
the unit reward provided for the edge aggregation performance to ensure the
interests of cloud servers. Furthermore, numerical results indicate that the
proposed algorithms can achieve better performance than the benchmark schemes
Direct observation of two-dimensional small polarons at correlated oxide interface
Two-dimensional (2D) perovskite oxide interfaces are ideal systems where
diverse emergent properties can be uncovered.The formation and modification of
polaronic properties due to short-range strong charge-lattice interactions of
2D interfaces remains hugely intriguing.Here, we report the direct observation
of small-polarons at the LaAlO3/SrTiO3 (LAO/STO) conducting interface using
high-resolution spectroscopic ellipsometry.First-principles investigations
further reveals that strong coupling between the interfacial electrons and the
Ti-lattice result in the formation of localized 2D small polarons.These
findings resolve the longstanding issue where the excess experimentally
measured interfacial carrier density is significantly lower than theoretically
predicted values.The charge-phonon induced lattice distortion further provides
an analogue to the superconductive states in magic-angle twisted bilayer
graphene attributed to the many-body correlations induced by broken periodic
lattice symmetry.Our study sheds light on the multifaceted complexity of broken
periodic lattice induced quasi-particle effects and its relationship with
superconductivity