24 research outputs found
PPBFL: A Privacy Protected Blockchain-based Federated Learning Model
With the rapid development of machine learning and a growing concern for data
privacy, federated learning has become a focal point of attention. However,
attacks on model parameters and a lack of incentive mechanisms hinder the
effectiveness of federated learning. Therefore, we propose A Privacy Protected
Blockchain-based Federated Learning Model (PPBFL) to enhance the security of
federated learning and encourage active participation of nodes in model
training. Blockchain technology ensures the integrity of model parameters
stored in the InterPlanetary File System (IPFS), providing protection against
tampering. Within the blockchain, we introduce a Proof of Training Work (PoTW)
consensus algorithm tailored for federated learning, aiming to incentive
training nodes. This algorithm rewards nodes with greater computational power,
promoting increased participation and effort in the federated learning process.
A novel adaptive differential privacy algorithm is simultaneously applied to
local and global models. This safeguards the privacy of local data at training
clients, preventing malicious nodes from launching inference attacks.
Additionally, it enhances the security of the global model, preventing
potential security degradation resulting from the combination of numerous local
models. The possibility of security degradation is derived from the composition
theorem. By introducing reverse noise in the global model, a zero-bias estimate
of differential privacy noise between local and global models is achieved.
Furthermore, we propose a new mix transactions mechanism utilizing ring
signature technology to better protect the identity privacy of local training
clients. Security analysis and experimental results demonstrate that PPBFL,
compared to baseline methods, not only exhibits superior model performance but
also achieves higher security
Safe DreamerV3: Safe Reinforcement Learning with World Models
The widespread application of Reinforcement Learning (RL) in real-world
situations is yet to come to fruition, largely as a result of its failure to
satisfy the essential safety demands of such systems. Existing safe
reinforcement learning (SafeRL) methods, employing cost functions to enhance
safety, fail to achieve zero-cost in complex scenarios, including vision-only
tasks, even with comprehensive data sampling and training. To address this, we
introduce Safe DreamerV3, a novel algorithm that integrates both
Lagrangian-based and planning-based methods within a world model. Our
methodology represents a significant advancement in SafeRL as the first
algorithm to achieve nearly zero-cost in both low-dimensional and vision-only
tasks within the Safety-Gymnasium benchmark. Our project website can be found
in: https://sites.google.com/view/safedreamerv3
HL-DPoS: An Enhanced Anti-Long-Range Attack DPoS Algorithm
The consensus algorithm is crucial in blockchain for ensuring the validity
and security of transactions across the decentralized network. However,
achieving consensus among nodes and packaging blocks in blockchain networks is
a complex task that requires efficient and secure consensus algorithms. The
DPoS consensus algorithm has emerged as a popular choice due to its fast
transaction processing and high throughput. Despite these advantages, the
algorithm still suffers from weaknesses such as centralization and
vulnerability to long-range attacks, which can compromise the integrity of the
blockchain network.
To combat these problems, we developed an Enhanced Anti-Long-Range Attack
DPoS algorithm (HL-DPoS). First, we split nodes into pieces to reduce
centralization issues while giving witness nodes the power to report and
benefit from malicious node's reports, maintaining high efficiency and high
security. Second, we propose a validation method in HL-DPoS that compares
consensuses transactions with the longest chain to detect long-range attacks.
Algorithm analysis and simulation experiment results demonstrate that our
HL-DPoS consensus algorithm improves security while achieving better consensus
performance
Configured Quantum Reservoir Computing for Multi-Task Machine Learning
Amidst the rapid advancements in experimental technology,
noise-intermediate-scale quantum (NISQ) devices have become increasingly
programmable, offering versatile opportunities to leverage quantum
computational advantage. Here we explore the intricate dynamics of programmable
NISQ devices for quantum reservoir computing. Using a genetic algorithm to
configure the quantum reservoir dynamics, we systematically enhance the
learning performance. Remarkably, a single configured quantum reservoir can
simultaneously learn multiple tasks, including a synthetic oscillatory network
of transcriptional regulators, chaotic motifs in gene regulatory networks, and
the fractional-order Chua's circuit. Our configured quantum reservoir computing
yields highly precise predictions for these learning tasks, outperforming
classical reservoir computing. We also test the configured quantum reservoir
computing in foreign exchange (FX) market applications and demonstrate its
capability to capture the stochastic evolution of the exchange rates with
significantly greater accuracy than classical reservoir computing approaches.
Through comparison with classical reservoir computing, we highlight the unique
role of quantum coherence in the quantum reservoir, which underpins its
exceptional learning performance. Our findings suggest the exciting potential
of configured quantum reservoir computing for exploiting the quantum
computation power of NISQ devices in developing artificial general
intelligence
Adaptive Operator Quantum-Behaved Pigeon-Inspired Optimization Algorithm with Application to UAV Path Planning
Path planning of unmanned aerial vehicles (UAVs) in threatening and adversarial areas is a constrained nonlinear optimal problem which takes a great amount of static and dynamic constraints into account. Quantum-behaved pigeon-inspired optimization (QPIO) has been widely applied to such nonlinear problems. However, conventional QPIO is suffering low global convergence speed and local optimum. In order to solve the above problems, an improved QPIO algorithm, adaptive operator QPIO, is proposed in this paper. Firstly, a new initialization process based on logistic mapping method is introduced to generate the initial population of the pigeon-swarm. After that, to improve the performance of the map and compass operation, the factor parameter will be adaptively updated in each iteration, which can balance the ability between global and local search. In the final landmark operation, the gradual decreasing pigeon population-updating strategy is introduced to prevent premature convergence and local optimum. Finally, the demonstration of the proposed algorithm on UAV path planning problem is presented, and the comparison result indicates that the performance of our algorithm is better than that of particle swarm optimization (PSO), pigeon-inspired optimization (PIO), and its variants, in terms of convergence and accuracy
FedHGCDroid: An Adaptive Multi-Dimensional Federated Learning for Privacy-Preserving Android Malware Classification
With the popularity of Android and its open source, the Android platform has become an attractive target for hackers, and the detection and classification of malware has become a research hotspot. Existing malware classification methods rely on complex manual operation or large-volume high-quality training data. However, malware data collected by security providers contains user privacy information, such as user identity and behavior habit information. The increasing concern for user privacy poses a challenge to the current malware classification scheme. Based on this problem, we propose a new android malware classification scheme based on Federated learning, named FedHGCDroid, which classifies malware on Android clients in a privacy-protected manner. Firstly, we use a convolutional neural network and graph neural network to design a novel multi-dimensional malware classification model HGCDroid, which can effectively extract malicious behavior features to classify the malware accurately. Secondly, we introduce an FL framework to enable distributed Android clients to collaboratively train a comprehensive Android malware classification model in a privacy-preserving way. Finally, to adapt to the non-IID distribution of malware on Android clients, we propose a contribution degree-based adaptive classifier training mechanism FedAdapt to improve the adaptability of the malware classifier based on Federated learning. Comprehensive experimental studies on the Androzoo dataset (under different non-IID data settings) show that the FedHGCDroid achieves more adaptability and higher accuracy than the other state-of-the-art methods
Cluster-Based Structural Redundancy Identification for Neural Network Compression
The increasingly large structure of neural networks makes it difficult to deploy on edge devices with limited computing resources. Network pruning has become one of the most successful model compression methods in recent years. Existing works typically compress models based on importance, removing unimportant filters. This paper reconsiders model pruning from the perspective of structural redundancy, claiming that identifying functionally similar filters plays a more important role, and proposes a model pruning framework for clustering-based redundancy identification. First, we perform cluster analysis on the filters of each layer to generate similar sets with different functions. We then propose a criterion for identifying redundant filters within similar sets. Finally, we propose a pruning scheme that automatically determines the pruning rate of each layer. Extensive experiments on various benchmark network architectures and datasets demonstrate the effectiveness of our proposed framework
Innovative Deep Neural Network Modeling for Fine-Grained Chinese Entity Recognition
Named entity recognition (NER) is a basic but crucial task in the field of natural language processing (NLP) and big data analysis. The recognition of named entities based on Chinese is more complicated and difficult than English, which makes the task of NER in Chinese more challenging. In particular, fine-grained named entity recognition is more challenging than traditional named entity recognition tasks, mainly because fine-grained tasks have higher requirements for the ability of automatic feature extraction and information representation of deep neural models. In this paper, we propose an innovative neural network model named En2BiLSTM-CRF to improve the effect of fine-grained Chinese entity recognition tasks. This proposed model including the initial encoding layer, the enhanced encoding layer, and the decoding layer combines the advantages of pre-training model encoding, dual bidirectional long short-term memory (BiLSTM) networks, and a residual connection mechanism. Hence, it can encode information multiple times and extract contextual features hierarchically. We conducted sufficient experiments on two representative datasets using multiple important metrics and compared them with other advanced baselines. We present promising results showing that our proposed En2BiLSTM-CRF has better performance as well as better generalization ability in both fine-grained and coarse-grained Chinese entity recognition tasks