Search Results for author:
Found 23 papers, 4 papers with code
MiniMax-01: Scaling Foundation Models with Lightning Attention
This approach enables us to conduct efficient training and inference on models with hundreds of billions of parameters across contexts spanning millions of tokens.
BGTplanner: Maximizing Training Accuracy for Differentially Private Federated Recommenders via Strategic Privacy Budget Allocation
To mitigate the rising concern about privacy leakage, the federated recommender (FR) paradigm emerges, in which decentralized clients co-train the recommendation model without exposing their raw user-item rating data.
A Communication and Computation Efficient Fully First-order Method for Decentralized Bilevel Optimization
Bilevel optimization, crucial for hyperparameter tuning, meta-learning and reinforcement learning, remains less explored in the decentralized learning paradigm, such as decentralized federated learning (DFL).
Snapshot: Towards Application-centered Models for Pedestrian Trajectory Prediction in Urban Traffic Environments
This paper explores pedestrian trajectory prediction in urban traffic while focusing on both model accuracy and real-world applicability.
Mitigating Noise Detriment in Differentially Private Federated Learning with Model Pre-training
Inspired by these advantages, we are the first to explore how model pre-training can mitigate noise detriment in differentially private federated learning (DPFL).
The Power of Bias: Optimizing Client Selection in Federated Learning with Heterogeneous Differential Privacy
Based on convergence analysis, we formulate the client selection problem to minimize the value of loss function in DPFL with heterogeneous privacy, which is a convex optimization problem and can be solved efficiently.
Fed-CVLC: Compressing Federated Learning Communications with Variable-Length Codes
In Federated Learning (FL) paradigm, a parameter server (PS) concurrently communicates with distributed participating clients for model collection, update aggregation, and model distribution over multiple rounds, without touching private data owned by individual clients.
Expediting In-Network Federated Learning by Voting-Based Consensus Model Compression
Recently, federated learning (FL) has gained momentum because of its capability in preserving data privacy.
pFedSim: Similarity-Aware Model Aggregation Towards Personalized Federated Learning
More specifically, we decouple a NN model into a personalized feature extractor, obtained by aggregating models from similar clients, and a classifier, which is obtained by local training and used to estimate client similarity.
A Survey of Federated Evaluation in Federated Learning
In traditional machine learning, it is trivial to conduct model evaluation since all data samples are managed centrally by a server.
FedDWA: Personalized Federated Learning with Dynamic Weight Adjustment
In this paper, we propose a new PFL algorithm called \emph{FedDWA (Federated Learning with Dynamic Weight Adjustment)} to address the above problem, which leverages the parameter server (PS) to compute personalized aggregation weights based on collected models from clients.
BARA: Efficient Incentive Mechanism with Online Reward Budget Allocation in Cross-Silo Federated Learning
The challenge of this problem lies in the opaque feedback between reward budget allocation and model utility improvement of FL, making the optimal reward budget allocation complicated.
Edge-Based Video Analytics: A Survey
Edge computing has been getting a momentum with ever-increasing data at the edge of the network.
Deep Hierarchy Quantization Compression algorithm based on Dynamic Sampling
Unlike traditional distributed machine learning, federated learning stores data locally for training and then aggregates the models on the server, which solves the data security problem that may arise in traditional distributed machine learning.
Boost Decentralized Federated Learning in Vehicular Networks by Diversifying Data Sources
To boost the convergence of DFL, a vehicle tunes the aggregation weight of each data source by minimizing the KL divergence of its state vector, and its effectiveness in diversifying data sources can be theoretically proved.
A Fast Blockchain-based Federated Learning Framework with Compressed Communications
Recently, blockchain-based federated learning (BFL) has attracted intensive research attention due to that the training process is auditable and the architecture is serverless avoiding the single point failure of the parameter server in vanilla federated learning (VFL).
Optimal Rate Adaption in Federated Learning with Compressed Communications
Federated Learning (FL) incurs high communication overhead, which can be greatly alleviated by compression for model updates.
Optimizing the Numbers of Queries and Replies in Federated Learning with Differential Privacy
In this work, we study a crucial question which has been vastly overlooked by existing works: what are the optimal numbers of queries and replies in FL with DP so that the final model accuracy is maximized.
Slashing Communication Traffic in Federated Learning by Transmitting Clustered Model Updates
Then, we further propose the boosted MUCSC (B-MUCSC) algorithm, a biased compression algorithm that can achieve an extremely high compression rate by grouping insignificant model updates into a super cluster.
Robust Sensor Fusion Algorithms Against Voice Command Attacks in Autonomous Vehicles
This technology enables drivers to use voice commands to control the vehicle and will be soon available in Advanced Driver Assistance Systems (ADAS).
Virtual Reality: A Survey of Enabling Technologies and its Applications in IoT
Virtual Reality (VR) has shown great potential to revolutionize the market by providing users immersive experiences with freedom of movement.
Networking and Internet Architecture
On the Practicality of Differential Privacy in Federated Learning by Tuning Iteration Times
Then, we theoretically derive: 1) the conditions for the DP based FedAvg to converge as the number of global iterations (GI) approaches infinity; 2) the method to set the number of local iterations (LI) to minimize the negative influence of DP noises.
Mitigating Sybil Attacks on Differential Privacy based Federated Learning
The state-of-the-art privacy-preserving technique in the context of federated learning is user-level differential privacy.
Cryptography and Security Distributed, Parallel, and Cluster Computing
