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Found 23 papers, 4 papers with code
FedMeS: Personalized Federated Continual Learning Leveraging Local Memory
We focus on the problem of Personalized Federated Continual Learning (PFCL): a group of distributed clients, each with a sequence of local tasks on arbitrary data distributions, collaborate through a central server to train a personalized model at each client, with the model expected to achieve good performance on all local tasks.
InternVideo2: Scaling Video Foundation Models for Multimodal Video Understanding
We introduce InternVideo2, a new video foundation model (ViFM) that achieves the state-of-the-art performance in action recognition, video-text tasks, and video-centric dialogue.
Ranked #1 on
Audio Classification
on ESC-50
(using extra training data)
FedVS: Straggler-Resilient and Privacy-Preserving Vertical Federated Learning for Split Models
The problem of split VFL is to train a model split between the server and the clients.
Chameleon: Adapting to Peer Images for Planting Durable Backdoors in Federated Learning
In a federated learning (FL) system, distributed clients upload their local models to a central server to aggregate into a global model.
Scene Style Text Editing
Specifically, QuadNet consists of four parts, namely background inpainting, style encoder, content encoder, and fusion generator.
Boosting Cross-task Transferability of Adversarial Patches with Visual Relations
The transferability of adversarial examples is a crucial aspect of evaluating the robustness of deep learning systems, particularly in black-box scenarios.
Stochastic Coded Federated Learning: Theoretical Analysis and Incentive Mechanism Design
During training, the server computes gradients on the global coded dataset to compensate for the missing model updates of the straggling devices.
DReS-FL: Dropout-Resilient Secure Federated Learning for Non-IID Clients via Secret Data Sharing
Federated learning (FL) strives to enable collaborative training of machine learning models without centrally collecting clients' private data.
Secure Embedding Aggregation for Federated Representation Learning
We consider a federated representation learning framework, where with the assistance of a central server, a group of $N$ distributed clients train collaboratively over their private data, for the representations (or embeddings) of a set of entities (e. g., users in a social network).
Secure Federated Clustering
We consider a foundational unsupervised learning task of $k$-means data clustering, in a federated learning (FL) setting consisting of a central server and many distributed clients.
Generalized Lagrange Coded Computing: A Flexible Computation-Communication Tradeoff for Resilient, Secure, and Private Computation
We consider the problem of evaluating arbitrary multivariate polynomials over a massive dataset containing multiple inputs, on a distributed computing system with a master node and multiple worker nodes.
SwiftAgg+: Achieving Asymptotically Optimal Communication Loads in Secure Aggregation for Federated Learning
We propose SwiftAgg+, a novel secure aggregation protocol for federated learning systems, where a central server aggregates local models of $N \in \mathbb{N}$ distributed users, each of size $L \in \mathbb{N}$, trained on their local data, in a privacy-preserving manner.
SwiftAgg: Communication-Efficient and Dropout-Resistant Secure Aggregation for Federated Learning with Worst-Case Security Guarantees
We propose SwiftAgg, a novel secure aggregation protocol for federated learning systems, where a central server aggregates local models of $N$ distributed users, each of size $L$, trained on their local data, in a privacy-preserving manner.
Stochastic Coded Federated Learning with Convergence and Privacy Guarantees
Federated learning (FL) has attracted much attention as a privacy-preserving distributed machine learning framework, where many clients collaboratively train a machine learning model by exchanging model updates with a parameter server instead of sharing their raw data.
LightSecAgg: a Lightweight and Versatile Design for Secure Aggregation in Federated Learning
We also demonstrate that, unlike existing schemes, LightSecAgg can be applied to secure aggregation in the asynchronous FL setting.
OmniLytics: A Blockchain-based Secure Data Market for Decentralized Machine Learning
Utilizing OmniLytics, many distributed data owners can contribute their private data to collectively train an ML model requested by some model owners, and receive compensation for data contribution.
FedML: A Research Library and Benchmark for Federated Machine Learning
Federated learning (FL) is a rapidly growing research field in machine learning.
Pipe-SGD: A Decentralized Pipelined SGD Framework for Distributed Deep Net Training
Distributed training of deep nets is an important technique to address some of the present day computing challenges like memory consumption and computational demands.
GradiVeQ: Vector Quantization for Bandwidth-Efficient Gradient Aggregation in Distributed CNN Training
Data parallelism can boost the training speed of convolutional neural networks (CNN), but could suffer from significant communication costs caused by gradient aggregation.
PolyShard: Coded Sharding Achieves Linearly Scaling Efficiency and Security Simultaneously
In particular, we propose PolyShard: ``polynomially coded sharding'' scheme that achieves information-theoretic upper bounds on the efficiency of the storage, system throughput, as well as on trust, thus enabling a truly scalable system.
Cryptography and Security Distributed, Parallel, and Cluster Computing Information Theory Information Theory
Lagrange Coded Computing: Optimal Design for Resiliency, Security and Privacy
We consider a scenario involving computations over a massive dataset stored distributedly across multiple workers, which is at the core of distributed learning algorithms.
Polynomially Coded Regression: Optimal Straggler Mitigation via Data Encoding
In particular, PCR requires a recovery threshold that scales inversely proportionally with the amount of computation/storage available at each worker.
Coded TeraSort
We focus on sorting, which is the building block of many machine learning algorithms, and propose a novel distributed sorting algorithm, named Coded TeraSort, which substantially improves the execution time of the TeraSort benchmark in Hadoop MapReduce.
Distributed, Parallel, and Cluster Computing Information Theory Information Theory
