Search Results for author:
Found 89 papers, 26 papers with code
ActPerFL: Active Personalized Federated Learning
Inspired by Bayesian hierarchical models, we develop ActPerFL, a self-aware personalized FL method where each client can automatically balance the training of its local personal model and the global model that implicitly contributes to other clients’ training.
CryptoMamba: Leveraging State Space Models for Accurate Bitcoin Price Prediction
Predicting Bitcoin price remains a challenging problem due to the high volatility and complex non-linear dynamics of cryptocurrency markets.
Alopex: A Computational Framework for Enabling On-Device Function Calls with LLMs
The rapid advancement of Large Language Models (LLMs) has led to their increased integration into mobile devices for personalized assistance, which enables LLMs to call external API functions to enhance their performance.
Enabling Efficient On-Device Fine-Tuning of LLMs Using Only Inference Engines
However, directly applying ZO methods on edge devices is impractical due to the high computational cost of multiple model perturbations required to achieve accuracy improvements.
ModalityMirror: Improving Audio Classification in Modality Heterogeneity Federated Learning with Multimodal Distillation
Multimodal Federated Learning frequently encounters challenges of client modality heterogeneity, leading to undesired performances for secondary modality in multimodal learning.
TensorOpera Router: A Multi-Model Router for Efficient LLM Inference
With the rapid growth of Large Language Models (LLMs) across various domains, numerous new LLMs have emerged, each possessing domain-specific expertise.
ScaleLLM: A Resource-Frugal LLM Serving Framework by Optimizing End-to-End Efficiency
Large language models (LLMs) have surged in popularity and are extensively used in commercial applications, where the efficiency of model serving is crucial for the user experience.
AICircuit: A Multi-Level Dataset and Benchmark for AI-Driven Analog Integrated Circuit Design
A major obstacle for bearing the power of machine learning in circuit design is the availability of a generic and diverse dataset, along with robust metrics, which are essential for thoroughly evaluating and improving machine learning algorithms in the analog and radio-frequency circuit domain.
CroMo-Mixup: Augmenting Cross-Model Representations for Continual Self-Supervised Learning
Two main challenges of continual learning are catastrophic forgetting and task confusion.
Embracing Federated Learning: Enabling Weak Client Participation via Partial Model Training
In Federated Learning (FL), clients may have weak devices that cannot train the full model or even hold it in their memory space.
Do Not Design, Learn: A Trainable Scoring Function for Uncertainty Estimation in Generative LLMs
Uncertainty estimation (UE) of generative large language models (LLMs) is crucial for evaluating the reliability of generated sequences.
Creating a Lens of Chinese Culture: A Multimodal Dataset for Chinese Pun Rebus Art Understanding
Large vision-language models (VLMs) have demonstrated remarkable abilities in understanding everyday content.
Maverick-Aware Shapley Valuation for Client Selection in Federated Learning
In this paper, we first design a Maverick-aware Shapley valuation that fairly evaluates the contribution of Mavericks.
Revisiting OPRO: The Limitations of Small-Scale LLMs as Optimizers
Numerous recent works aim to enhance the efficacy of Large Language Models (LLMs) through strategic prompting.
Federated Learning Privacy: Attacks, Defenses, Applications, and Policy Landscape - A Survey
In this survey paper, we provide a comprehensive literature review of the different privacy attacks and defense methods in FL.
Differentially Private Federated Learning without Noise Addition: When is it Possible?
However, we further demonstrate that in practice, these conditions are almost unlikely to hold and hence additional noise added in model updates is still required in order for SA in FL to achieve DP.
Hawk: Accurate and Fast Privacy-Preserving Machine Learning Using Secure Lookup Table Computation
In this work, we design and implement new privacy-preserving machine learning protocols for logistic regression and neural network models.
Edge Private Graph Neural Networks with Singular Value Perturbation
Eclipse adds noise to the low-rank singular values instead of the entire graph, thereby preserving the graph privacy while still maintaining enough of the graph structure to maintain model utility.
Ethos: Rectifying Language Models in Orthogonal Parameter Space
Evaluations show Ethos is more effective in removing undesired knowledge and maintaining the overall model performance compared to current task arithmetic methods.
ATP: Enabling Fast LLM Serving via Attention on Top Principal Keys
In particular, ATP barely loses accuracy with only $1/2$ principal keys, and only incurs around $2\%$ accuracy drops with $1/4$ principal keys.
MARS: Meaning-Aware Response Scoring for Uncertainty Estimation in Generative LLMs
In this work, we propose Meaning-Aware Response Scoring (MARS) as an alternative to length-normalized scoring for UE methods.
All Rivers Run to the Sea: Private Learning with Asymmetric Flows
The main part flows into a small model while the residuals are offloaded to a large model.
Kick Bad Guys Out! Conditionally Activated Anomaly Detection in Federated Learning with Zero-Knowledge Proof Verification
Federated Learning (FL) systems are susceptible to adversarial attacks, where malicious clients submit poisoned models to disrupt the convergence or plant backdoors that cause the global model to misclassify some samples.
FedAIoT: A Federated Learning Benchmark for Artificial Intelligence of Things
However, most existing FL works do not use datasets collected from authentic IoT devices and thus do not capture unique modalities and inherent challenges of IoT data.
Federated Orthogonal Training: Mitigating Global Catastrophic Forgetting in Continual Federated Learning
We propose a novel method, Federated Orthogonal Training (FOT), to overcome these drawbacks and address the global catastrophic forgetting in CFL.
SLoRA: Federated Parameter Efficient Fine-Tuning of Language Models
In the absence of centralized data, Federated Learning (FL) can benefit from distributed and private data of the FL edge clients for fine-tuning.
mL-BFGS: A Momentum-based L-BFGS for Distributed Large-Scale Neural Network Optimization
Quasi-Newton methods still face significant challenges in training large-scale neural networks due to additional compute costs in the Hessian related computations and instability issues in stochastic training.
Don't Memorize; Mimic The Past: Federated Class Incremental Learning Without Episodic Memory
Deep learning models are prone to forgetting information learned in the past when trained on new data.
FedMultimodal: A Benchmark For Multimodal Federated Learning
In order to facilitate the research in multimodal FL, we introduce FedMultimodal, the first FL benchmark for multimodal learning covering five representative multimodal applications from ten commonly used datasets with a total of eight unique modalities.
FedSecurity: Benchmarking Attacks and Defenses in Federated Learning and Federated LLMs
This paper introduces FedSecurity, an end-to-end benchmark that serves as a supplementary component of the FedML library for simulating adversarial attacks and corresponding defense mechanisms in Federated Learning (FL).
GPT-FL: Generative Pre-trained Model-Assisted Federated Learning
Through comprehensive ablation analysis across various data modalities, we discover that the downstream model generated by synthetic data plays a crucial role in controlling the direction of gradient diversity during FL training, which enhances convergence speed and contributes to the notable accuracy boost observed with GPT-FL.
Federated Alternate Training (FAT): Leveraging Unannotated Data Silos in Federated Segmentation for Medical Imaging
It is a distributed learning framework naturally suitable for privacy-sensitive medical imaging datasets.
TimelyFL: Heterogeneity-aware Asynchronous Federated Learning with Adaptive Partial Training
However, we show empirically that this method can lead to a substantial drop in training accuracy as well as a slower convergence rate.
The Resource Problem of Using Linear Layer Leakage Attack in Federated Learning
We show that this resource overhead is caused by an incorrect perspective in all prior work that treats an attack on an aggregate update in the same way as an individual update with a larger batch size.
LOKI: Large-scale Data Reconstruction Attack against Federated Learning through Model Manipulation
When both FedAVG and secure aggregation are used, there is no current method that is able to attack multiple clients concurrently in a federated learning setting.
FedML-HE: An Efficient Homomorphic-Encryption-Based Privacy-Preserving Federated Learning System
Federated Learning trains machine learning models on distributed devices by aggregating local model updates instead of local data.
FedML Parrot: A Scalable Federated Learning System via Heterogeneity-aware Scheduling on Sequential and Hierarchical Training
It improves the training efficiency, remarkably relaxes the requirements on the hardware, and supports efficient large-scale FL experiments with stateful clients by: (1) sequential training clients on devices; (2) decomposing original aggregation into local and global aggregation on devices and server respectively; (3) scheduling tasks to mitigate straggler problems and enhance computing utility; (4) distributed client state manager to support various FL algorithms.
Proof-of-Contribution-Based Design for Collaborative Machine Learning on Blockchain
Our goal is to design a data marketplace for such decentralized collaborative/federated learning applications that simultaneously provides i) proof-of-contribution based reward allocation so that the trainers are compensated based on their contributions to the trained model; ii) privacy-preserving decentralized model training by avoiding any data movement from data owners; iii) robustness against malicious parties (e. g., trainers aiming to poison the model); iv) verifiability in the sense that the integrity, i. e., correctness, of all computations in the data market protocol including contribution assessment and outlier detection are verifiable through zero-knowledge proofs; and v) efficient and universal design.
Federated Analytics: A survey
Federated analytics (FA) is a privacy-preserving framework for computing data analytics over multiple remote parties (e. g., mobile devices) or silo-ed institutional entities (e. g., hospitals, banks) without sharing the data among parties.
SMILE: Scaling Mixture-of-Experts with Efficient Bi-level Routing
The mixture of Expert (MoE) parallelism is a recent advancement that scales up the model size with constant computational cost.
FLamby: Datasets and Benchmarks for Cross-Silo Federated Learning in Realistic Healthcare Settings
In this work, we propose a novel cross-silo dataset suite focused on healthcare, FLamby (Federated Learning AMple Benchmark of Your cross-silo strategies), to bridge the gap between theory and practice of cross-silo FL.
The Geometry of Self-supervised Learning Models and its Impact on Transfer Learning
Self-supervised learning (SSL) has emerged as a desirable paradigm in computer vision due to the inability of supervised models to learn representations that can generalize in domains with limited labels.
Federated Learning of Large Models at the Edge via Principal Sub-Model Training
However, the heterogeneous-client setting requires some clients to train full model, which is not aligned with the resource-constrained setting; while the latter ones break privacy promises in FL when sharing intermediate representations or labels with the server.
Lottery Aware Sparsity Hunting: Enabling Federated Learning on Resource-Limited Edge
A possible solution to this problem is to utilize off-the-shelf sparse learning algorithms at the clients to meet their resource budget.
How Much Privacy Does Federated Learning with Secure Aggregation Guarantee?
While SA ensures no additional information is leaked about the individual model update beyond the aggregated model update, there are no formal guarantees on how much privacy FL with SA can actually offer; as information about the individual dataset can still potentially leak through the aggregated model computed at the server.
One Model to Unite Them All: Personalized Federated Learning of Multi-Contrast MRI Synthesis
As such, pFLSynth enables training of a unified synthesis model that can reliably generalize across multiple sites and translation tasks.
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.
Toward a Geometrical Understanding of Self-supervised Contrastive Learning
When used for transfer learning, the projector is discarded since empirical results show that its representation generalizes more poorly than the encoder's.
Federated Learning with Noisy User Feedback
Federated learning (FL) has recently emerged as a method for training ML models on edge devices using sensitive user data and is seen as a way to mitigate concerns over data privacy.
Self-Aware Personalized Federated Learning
In the context of personalized federated learning (FL), the critical challenge is to balance local model improvement and global model tuning when the personal and global objectives may not be exactly aligned.
Federated Learning of Generative Image Priors for MRI Reconstruction
Specificity in the prior is preserved via a mapper subnetwork that produces site-specific latents.
Learnings from Federated Learning in the Real world
In this study, we evaluate the impact of such idiosyncrasies on Natural Language Understanding (NLU) models trained using FL.
FedSpace: An Efficient Federated Learning Framework at Satellites and Ground Stations
To address these challenges, we leverage Federated Learning (FL), where ground stations and satellites collaboratively train a global ML model without sharing the captured images on the satellites.
Federated Learning Challenges and Opportunities: An Outlook
Federated learning (FL) has been developed as a promising framework to leverage the resources of edge devices, enhance customers' privacy, comply with regulations, and reduce development costs.
Partial Model Averaging in Federated Learning: Performance Guarantees and Benefits
We propose a partial model averaging framework that mitigates the model discrepancy issue in Federated Learning.
SPIDER: Searching Personalized Neural Architecture for Federated Learning
However, given that clients' data are invisible to the server and data distributions are non-identical across clients, a predefined architecture discovered in a centralized setting may not be an optimal solution for all the clients in FL.
FedCV: A Federated Learning Framework for Diverse Computer Vision Tasks
To bridge the gap and facilitate the development of FL for computer vision tasks, in this work, we propose a federated learning library and benchmarking framework, named FedCV, to evaluate FL on the three most representative computer vision tasks: image classification, image segmentation, and object detection.
Federated Learning for Internet of Things: Applications, Challenges, and Opportunities
In this paper, we will discuss the opportunities and challenges of FL in IoT platforms, as well as how it can enable diverse IoT applications.
Layer-wise Adaptive Model Aggregation for Scalable Federated Learning
In Federated Learning, a common approach for aggregating local models across clients is periodic averaging of the full model parameters.
SSFL: Tackling Label Deficiency in Federated Learning via Personalized Self-Supervision
In this paper we propose self-supervised federated learning (SSFL), a unified self-supervised and personalized federated learning framework, and a series of algorithms under this framework which work towards addressing these challenges.
FairFed: Enabling Group Fairness in Federated Learning
Training ML models which are fair across different demographic groups is of critical importance due to the increased integration of ML in crucial decision-making scenarios such as healthcare and recruitment.
Fundamental Limits of Transfer Learning in Binary Classifications
Transfer learning is gaining traction as a promising technique to alleviate this barrier by utilizing the data of a related but different \emph{source} task to compensate for the lack of data in a \emph{target} task where there are few labeled training data.
SLIM-QN: A Stochastic, Light, Momentumized Quasi-Newton Optimizer for Deep Neural Networks
SLIM-QN addresses two key barriers in existing second-order methods for large-scale DNNs: 1) the high computational cost of obtaining the Hessian matrix and its inverse in every iteration (e. g. KFAC); 2) convergence instability due to stochastic training (e. g. L-BFGS).
FedNAS: Federated Deep Learning via Neural Architecture Search
Federated Learning (FL) is an effective learning framework used when data cannotbe centralized due to privacy, communication costs, and regulatory restrictions. While there have been many algorithmic advances in FL, significantly less effort hasbeen made on model development, and most works in FL employ predefined modelarchitectures discovered in the centralized environment.
Achieving Small-Batch Accuracy with Large-Batch Scalability via Adaptive Learning Rate Adjustment
The framework performs extra epochs using the large learning rate even after the loss is flattened.
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.
Private Retrieval, Computing and Learning: Recent Progress and Future Challenges
Most of our lives are conducted in the cyberspace.
Adaptive Verifiable Coded Computing: Towards Fast, Secure and Private Distributed Machine Learning
Much of the overhead in prior schemes comes from the fact that they tightly couple coding for all three problems into a single framework.
A Field Guide to Federated Optimization
Federated learning and analytics are a distributed approach for collaboratively learning models (or statistics) from decentralized data, motivated by and designed for privacy protection.
Federated Learning for Internet of Things: A Federated Learning Framework for On-device Anomaly Data Detection
In this paper, to further push forward this direction with a comprehensive study in both algorithm and system design, we build FedIoT platform that contains FedDetect algorithm for on-device anomaly data detection and a system design for realistic evaluation of federated learning on IoT devices.
Securing Secure Aggregation: Mitigating Multi-Round Privacy Leakage in Federated Learning
In fact, we show that the conventional random user selection strategies in FL lead to leaking users' individual models within number of rounds that is linear in the number of users.
SpreadGNN: Serverless Multi-task Federated Learning for Graph Neural Networks
This work proposes SpreadGNN, a novel multi-task federated training framework capable of operating in the presence of partial labels and absence of a central server for the first time in the literature.
FedNLP: Benchmarking Federated Learning Methods for Natural Language Processing Tasks
Increasing concerns and regulations about data privacy and sparsity necessitate the study of privacy-preserving, decentralized learning methods for natural language processing (NLP) tasks.
FedGraphNN: A Federated Learning System and Benchmark for Graph Neural Networks
FedGraphNN is built on a unified formulation of graph FL and contains a wide range of datasets from different domains, popular GNN models, and FL algorithms, with secure and efficient system support.
PipeTransformer: Automated Elastic Pipelining for Distributed Training of Transformers
PipeTransformer automatically adjusts the pipelining and data parallelism by identifying and freezing some layers during the training, and instead allocates resources for training of the remaining active layers.
Coded Computing for Low-Latency Federated Learning over Wireless Edge Networks
For minimizing the epoch deadline time at the MEC server, we provide a tractable approach for finding the amount of coding redundancy and the number of local data points that a client processes during training, by exploiting the statistical properties of compute as well as communication delays.
Group Knowledge Transfer: Federated Learning of Large CNNs at the Edge
However, the large model size impedes training on resource-constrained edge devices.
FedML: A Research Library and Benchmark for Federated Machine Learning
Federated learning (FL) is a rapidly growing research field in machine learning.
Towards Non-I.I.D. and Invisible Data with FedNAS: Federated Deep Learning via Neural Architecture Search
Federated Learning (FL) has been proved to be an effective learning framework when data cannot be centralized due to privacy, communication costs, and regulatory restrictions.
Train Where the Data is: A Case for Bandwidth Efficient Coded Training
Most of the model training is performed on high performance compute nodes and the training data is stored near these nodes for faster training.
Collage Inference: Achieving low tail latency during distributed image classification using coded redundancy models
In this work, we argue that MLaaS platforms also provide unique opportunities to cut the cost of redundancy.
Collage Inference: Using Coded Redundancy for Low Variance Distributed Image Classification
Deploying the collage-cnn models in the cloud, we demonstrate that the 99th percentile tail latency of inference can be reduced by 1. 2x to 2x compared to replication based approaches while providing high accuracy.
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.
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.
Distributed Solution of Large-Scale Linear Systems via Accelerated Projection-Based Consensus
In this paper, we consider a common scenario in which a taskmaster intends to solve a large-scale system of linear equations by distributing subsets of the equations among a number of computing machines/cores.
A Sampling Theory Perspective of Graph-based Semi-supervised Learning
In this work, we reinforce this connection by viewing the problem from a graph sampling theoretic perspective, where class indicator functions are treated as bandlimited graph signals (in the eigenvector basis of the graph Laplacian) and label prediction as a bandlimited reconstruction problem.
Active Learning for Community Detection in Stochastic Block Models
Our main result is to show that, under certain conditions, sampling the labels of a vanishingly small fraction of nodes (a number sub-linear in $n$) is sufficient for exact community detection even when $D(a, b)<1$.
