research
∙
09/03/2023
Federated Orthogonal Training: Mitigating Global Catastrophic Forgetting in Continual Federated Learning
Federated Learning (FL) has gained significant attraction due to its abi...
share
research
∙
08/12/2023
SLoRA: Federated Parameter Efficient Fine-Tuning of Language Models
Transfer learning via fine-tuning pre-trained transformer models has gai...
research
∙
07/25/2023
mL-BFGS: A Momentum-based L-BFGS for Distributed Large-Scale Neural Network Optimization
Quasi-Newton methods still face significant challenges in training large...
research
∙
07/02/2023
Don't Memorize; Mimic The Past: Federated Class Incremental Learning Without Episodic Memory
Deep learning models are prone to forgetting information learned in the ...
research
∙
06/08/2023
FedMLSecurity: A Benchmark for Attacks and Defenses in Federated Learning and LLMs
This paper introduces FedMLSecurity, a benchmark that simulates adversar...
research
∙
04/18/2023
Federated Alternate Training (FAT): Leveraging Unannotated Data Silos in Federated Segmentation for Medical Imaging
Federated Learning (FL) aims to train a machine learning (ML) model in a...
research
∙
03/31/2023
Secure Federated Learning against Model Poisoning Attacks via Client Filtering
Given the distributed nature, detecting and defending against the backdo...
research
∙
03/27/2023
The Resource Problem of Using Linear Layer Leakage Attack in Federated Learning
Secure aggregation promises a heightened level of privacy in federated l...
research
∙
03/21/2023
Secure Aggregation in Federated Learning is not Private: Leaking User Data at Large Scale through Model Modification
Security and privacy are important concerns in machine learning. End use...
research
∙
03/20/2023
FedML-HE: An Efficient Homomorphic-Encryption-Based Privacy-Preserving Federated Learning System
Federated Learning (FL) enables machine learning model training on distr...
research
∙
03/03/2023
FedML Parrot: A Scalable Federated Learning System via Heterogeneity-aware Scheduling on Sequential and Hierarchical Training
Federated Learning (FL) enables collaborations among clients for train m...
research
∙
02/27/2023
Proof-of-Contribution-Based Design for Collaborative Machine Learning on Blockchain
We consider a project (model) owner that would like to train a model by ...
research
∙
12/10/2022
SMILE: Scaling Mixture-of-Experts with Efficient Bi-level Routing
The mixture of Expert (MoE) parallelism is a recent advancement that sca...
research
∙
09/18/2022
The Geometry of Self-supervised Learning Models and its Impact on Transfer Learning
Self-supervised learning (SSL) has emerged as a desirable paradigm in co...
research
∙
08/03/2022
How Much Privacy Does Federated Learning with Secure Aggregation Guarantee?
Federated learning (FL) has attracted growing interest for enabling priv...
research
∙
07/13/2022
One Model to Unite Them All: Personalized Federated Learning of Multi-Contrast MRI Synthesis
Learning-based MRI translation involves a synthesis model that maps a so...
research
∙
05/31/2022
Secure Federated Clustering
We consider a foundational unsupervised learning task of k-means data cl...
research
∙
05/13/2022
Toward a Geometrical Understanding of Self-supervised Contrastive Learning
Self-supervised learning (SSL) is currently one of the premier technique...
research
∙
05/06/2022
Federated Learning with Noisy User Feedback
Machine Learning (ML) systems are getting increasingly popular, and driv...
research
∙
02/08/2022
Federated Learning of Generative Image Priors for MRI Reconstruction
Multi-institutional efforts can facilitate training of deep MRI reconstr...
research
∙
02/08/2022
Learnings from Federated Learning in the Real world
Federated Learning (FL) applied to real world data may suffer from sever...
research
∙
02/02/2022
FedSpace: An Efficient Federated Learning Framework at Satellites and Ground Stations
Large-scale deployments of low Earth orbit (LEO) satellites collect mass...
research
∙
01/11/2022
Partial Model Averaging in Federated Learning: Performance Guarantees and Benefits
Local Stochastic Gradient Descent (SGD) with periodic model averaging (F...
research
∙
12/27/2021
SPIDER: Searching Personalized Neural Architecture for Federated Learning
Federated learning (FL) is an efficient learning framework that assists ...
research
∙
11/22/2021
FedCV: A Federated Learning Framework for Diverse Computer Vision Tasks
Federated Learning (FL) is a distributed learning paradigm that can lear...
research
∙
10/19/2021
Layer-wise Adaptive Model Aggregation for Scalable Federated Learning
In Federated Learning, a common approach for aggregating local models ac...
research
∙
10/06/2021
SSFL: Tackling Label Deficiency in Federated Learning via Personalized Self-Supervision
Federated Learning (FL) is transforming the ML training ecosystem from a...
research
∙
10/04/2021
AsymML: An Asymmetric Decomposition Framework for Privacy-Preserving DNN Training and Inference
Leveraging parallel hardware (e.g. GPUs) to conduct deep neural network ...
research
∙
10/02/2021
FairFed: Enabling Group Fairness in Federated Learning
As machine learning becomes increasingly incorporated in crucial decisio...
research
∙
09/29/2021
LightSecAgg: Rethinking Secure Aggregation in Federated Learning
Secure model aggregation is a key component of federated learning (FL) t...
research
∙
07/30/2021
Private Retrieval, Computing and Learning: Recent Progress and Future Challenges
Most of our lives are conducted in the cyberspace. The human notion of p...
research
∙
07/27/2021
Verifiable Coded Computing: Towards Fast, Secure and Private Distributed Machine Learning
Stragglers, Byzantine workers, and data privacy are the main bottlenecks...
research
∙
06/15/2021
Federated Learning for Internet of Things: A Federated Learning Framework for On-device Anomaly Data Detection
Federated learning can be a promising solution for enabling IoT cybersec...
research
∙
06/07/2021
Securing Secure Aggregation: Mitigating Multi-Round Privacy Leakage in Federated Learning
Secure aggregation is a critical component in federated learning, which ...
research
∙
06/04/2021
SpreadGNN: Serverless Multi-task Federated Learning for Graph Neural Networks
Graph Neural Networks (GNNs) are the first choice methods for graph mach...
research
∙
04/14/2021
FedGraphNN: A Federated Learning System and Benchmark for Graph Neural Networks
Graph Neural Network (GNN) research is rapidly growing thanks to the cap...
research
∙
02/05/2021
PipeTransformer: Automated Elastic Pipelining for Distributed Training of Transformers
The size of Transformer models is growing at an unprecedented pace. It h...
research
∙
11/12/2020
Coded Computing for Low-Latency Federated Learning over Wireless Edge Networks
Federated learning enables training a global model from data located at ...
research
∙
07/28/2020
Group Knowledge Transfer: Collaborative Training of Large CNNs on the Edge
Scaling up the convolutional neural network (CNN) size (e.g., width, dep...
research
∙
04/18/2020
FedNAS: Federated Deep Learning via Neural Architecture Search
Federated Learning (FL) has been proved to be an effective learning fram...
research
∙
02/03/2020
InfoCommit: Information-Theoretic Polynomial Commitment and Verification
We introduce InfoCommit, a protocol for polynomial commitment and verifi...
research
∙
12/23/2019
Jupiter: A Networked Computing Architecture
In the era of Internet of Things, there is an increasing demand for netw...
research
∙
10/22/2019
Train Where the Data is: A Case for Bandwidth Efficient Coded Training
Training a machine learning model is both compute and data-intensive. Mo...
research
∙
10/02/2019
Coded Merkle Tree: Solving Data Availability Attacks in Blockchains
In this paper, we propose coded Merkle tree (CMT), a novel hash accumula...
research
∙
07/09/2019
Interactive Verifiable Polynomial Evaluation
Cloud computing platforms have created the possibility for computational...
research
∙
06/26/2019
Coded State Machine -- Scaling State Machine Execution under Byzantine Faults
We introduce an information-theoretic framework, named Coded State Machi...
research
∙
06/05/2019
Collage Inference: Achieving low tail latency during distributed image classification using coded redundancy models
Reducing the latency variance in machine learning inference is a key req...
research
∙
04/27/2019
Collage Inference: Tolerating Stragglers in Distributed Neural Network Inference using Coding
MLaaS (ML-as-a-Service) offerings by cloud computing platforms are becom...
research
∙
04/15/2019
Distributed Matrix Multiplication Using Speed Adaptive Coding
While performing distributed computations in today's cloud-based platfor...
research
∙
11/08/2018