Search Results for author:
Found 47 papers, 11 papers with code
Erasure Coded Neural Network Inference via Fisher Averaging
Erasure-coded computing has been successfully used in cloud systems to reduce tail latency caused by factors such as straggling servers and heterogeneous traffic variations.
Active Learning for WBAN-based Health Monitoring
These challenges make our problem fundamentally different from classical active learning, where unlabeled samples are free and labels can be queried in real time.
FADAS: Towards Federated Adaptive Asynchronous Optimization
Federated learning (FL) has emerged as a widely adopted training paradigm for privacy-preserving machine learning.
A New Theoretical Perspective on Data Heterogeneity in Federated Optimization
In particular, for many FL algorithms, the convergence rate grows dramatically when the number of local updates becomes large, especially when the product of the gradient divergence and local Lipschitz constant is large.
Adaptive Heterogeneous Client Sampling for Federated Learning over Wireless Networks
This paper aims to design an adaptive client sampling algorithm for FL over wireless networks that tackles both system and statistical heterogeneity to minimize the wall-clock convergence time.
Communication-Efficient Hybrid Federated Learning for E-health with Horizontal and Vertical Data Partitioning
In this paper, we provide a thorough study on an effective integration of HFL and VFL, to achieve communication efficiency and overcome the above limitations when data is both horizontally and vertically partitioned.
FedFisher: Leveraging Fisher Information for One-Shot Federated Learning
Standard federated learning (FL) algorithms typically require multiple rounds of communication between the server and the clients, which has several drawbacks, including requiring constant network connectivity, repeated investment of computational resources, and susceptibility to privacy attacks.
Federated Learning Priorities Under the European Union Artificial Intelligence Act
The age of AI regulation is upon us, with the European Union Artificial Intelligence Act (AI Act) leading the way.
A Survey on Efficient Federated Learning Methods for Foundation Model Training
We discuss the benefits and drawbacks of parameter-efficient fine-tuning (PEFT) for FL applications, elaborate on the readiness of FL frameworks to work with FMs, and provide future research opportunities on how to evaluate generative models in FL as well as the interplay of privacy and PEFT.
Federated Learning While Providing Model as a Service: Joint Training and Inference Optimization
Toward the challenges, we propose an online problem approximation to reduce the problem complexity and optimize the resources to balance the needs of model training and inference.
DePRL: Achieving Linear Convergence Speedup in Personalized Decentralized Learning with Shared Representations
Decentralized learning has emerged as an alternative method to the popular parameter-server framework which suffers from high communication burden, single-point failure and scalability issues due to the need of a central server.
Federated Fine-Tuning of LLMs on the Very Edge: The Good, the Bad, the Ugly
Large Language Models (LLM) and foundation models are popular as they offer new opportunities for individuals and businesses to improve natural language processing, interact with data, and retrieve information faster.
Straggler-Resilient Decentralized Learning via Adaptive Asynchronous Updates
With the increasing demand for large-scale training of machine learning models, fully decentralized optimization methods have recently been advocated as alternatives to the popular parameter server framework.
A Lightweight Method for Tackling Unknown Participation Statistics in Federated Averaging
In this paper, we address this problem by adapting the aggregation weights in federated averaging (FedAvg) based on the participation history of each client.
Adaptive Federated Pruning in Hierarchical Wireless Networks
We present the convergence analysis of an upper on the l2 norm of gradients for HFL with model pruning, analyze the computation and communication latency of the proposed model pruning scheme, and formulate an optimization problem to maximize the convergence rate under a given latency threshold by jointly optimizing the pruning ratio and wireless resource allocation.
LESS-VFL: Communication-Efficient Feature Selection for Vertical Federated Learning
As part of the training, the parties wish to remove unimportant features in the system to improve generalization, efficiency, and explainability.
Incentive Mechanism Design for Unbiased Federated Learning with Randomized Client Participation
Incentive mechanism is crucial for federated learning (FL) when rational clients do not have the same interests in the global model as the server.
Gradient-based Uncertainty Attribution for Explainable Bayesian Deep Learning
Predictions made by deep learning models are prone to data perturbations, adversarial attacks, and out-of-distribution inputs.
FedExP: Speeding Up Federated Averaging via Extrapolation
Federated Averaging (FedAvg) remains the most popular algorithm for Federated Learning (FL) optimization due to its simple implementation, stateless nature, and privacy guarantees combined with secure aggregation.
Federated Learning with Flexible Control
In this paper, we address this problem and propose FlexFL - an FL algorithm with multiple options that can be adjusted flexibly.
Compressed-VFL: Communication-Efficient Learning with Vertically Partitioned Data
Our work provides the first theoretical analysis of the effect message compression has on distributed training over vertically partitioned data.
A Unified Analysis of Federated Learning with Arbitrary Client Participation
Federated learning (FL) faces challenges of intermittent client availability and computation/communication efficiency.
Joint Coreset Construction and Quantization for Distributed Machine Learning
Coresets are small, weighted summaries of larger datasets, aiming at providing provable error bounds for machine learning (ML) tasks while significantly reducing the communication and computation costs.
Communication-Efficient Device Scheduling for Federated Learning Using Stochastic Optimization
Then, using the derived convergence bound, we use stochastic optimization to develop a new client selection and power allocation algorithm that minimizes a function of the convergence bound and the average communication time under a transmit power constraint.
KerGNNs: Interpretable Graph Neural Networks with Graph Kernels
We also show that the trained graph filters in KerGNNs can reveal the local graph structures of the dataset, which significantly improves the model interpretability compared with conventional GNN models.
Tackling System and Statistical Heterogeneity for Federated Learning with Adaptive Client Sampling
This paper aims to design an adaptive client sampling algorithm that tackles both system and statistical heterogeneity to minimize the wall-clock convergence time.
Cost-Effective Federated Learning in Mobile Edge Networks
Federated learning (FL) is a distributed learning paradigm that enables a large number of mobile devices to collaboratively learn a model under the coordination of a central server without sharing their raw data.
Cross-Silo Federated Learning for Multi-Tier Networks with Vertical and Horizontal Data Partitioning
Each silo contains a hub and a set of clients, with the silo's vertical data shard partitioned horizontally across its clients.
Communication-efficient k-Means for Edge-based Machine Learning
We consider the problem of computing the k-means centers for a large high-dimensional dataset in the context of edge-based machine learning, where data sources offload machine learning computation to nearby edge servers.
Tailored Learning-Based Scheduling for Kubernetes-Oriented Edge-Cloud System
In this paper, we introduce KaiS, a learning-based scheduling framework for such edge-cloud systems to improve the long-term throughput rate of request processing.
Continual Learning Without Knowing Task Identities: Rethinking Occam's Razor
Due to the catastrophic forgetting phenomenon of deep neural networks (DNNs), models trained in standard ways tend to forget what it has learned from previous tasks, especially when the new task is sufficiently different from the previous ones.
Cost-Effective Federated Learning Design
In this paper, we analyze how to design adaptive FL that optimally chooses these essential control variables to minimize the total cost while ensuring convergence.
Robustness and Diversity Seeking Data-Free Knowledge Distillation
Knowledge distillation (KD) has enabled remarkable progress in model compression and knowledge transfer.
Demystifying Why Local Aggregation Helps: Convergence Analysis of Hierarchical SGD
Furthermore, we extend our analytical approach based on "upward" and "downward" divergences to study the convergence for the general case of H-SGD with more than two levels, where the "sandwich behavior" still holds.
Online Learning of Facility Locations
In this paper, we provide a rigorous theoretical investigation of an online learning version of the Facility Location problem which is motivated by emerging problems in real-world applications.
Sharing Models or Coresets: A Study based on Membership Inference Attack
Distributed machine learning generally aims at training a global model based on distributed data without collecting all the data to a centralized location, where two different approaches have been proposed: collecting and aggregating local models (federated learning) and collecting and training over representative data summaries (coreset).
Federated Learning for Resource-Constrained IoT Devices: Panoramas and State-of-the-art
Nowadays, devices are equipped with advanced sensors with higher processing/computing capabilities.
Online Algorithms for Multi-shop Ski Rental with Machine Learned Advice
We obtain both deterministic and randomized online algorithms with provably improved performance when either a single or multiple ML predictions are used to make decisions.
Overcoming Noisy and Irrelevant Data in Federated Learning
A challenge is that among the large variety of data collected at each client, it is likely that only a subset is relevant for a learning task while the rest of data has a negative impact on model training.
Adaptive Gradient Sparsification for Efficient Federated Learning: An Online Learning Approach
Then, with the goal of minimizing the overall training time, we propose a novel online learning formulation and algorithm for automatically determining the near-optimal communication and computation trade-off that is controlled by the degree of gradient sparsity.
Model Pruning Enables Efficient Federated Learning on Edge Devices
To overcome this challenge, we propose PruneFL -- a novel FL approach with adaptive and distributed parameter pruning, which adapts the model size during FL to reduce both communication and computation overhead and minimize the overall training time, while maintaining a similar accuracy as the original model.
Distilling On-Device Intelligence at the Network Edge
Devices at the edge of wireless networks are the last mile data sources for machine learning (ML).
Online Collection and Forecasting of Resource Utilization in Large-Scale Distributed Systems
Monitoring the conditions of these nodes is important for system management purposes, which, however, can be extremely resource demanding as this requires collecting local measurements of each individual node and constantly sending those measurements to a central controller.
Robust Coreset Construction for Distributed Machine Learning
Coreset, which is a summary of the original dataset in the form of a small weighted set in the same sample space, provides a promising approach to enable machine learning over distributed data.
MaxHedge: Maximising a Maximum Online
We introduce a new online learning framework where, at each trial, the learner is required to select a subset of actions from a given known action set.
Adaptive Federated Learning in Resource Constrained Edge Computing Systems
Our focus is on a generic class of machine learning models that are trained using gradient-descent based approaches.
Dynamic Service Migration in Mobile Edge Computing Based on Markov Decision Process
In mobile edge computing, local edge servers can host cloud-based services, which reduces network overhead and latency but requires service migrations as users move to new locations.
Distributed, Parallel, and Cluster Computing Networking and Internet Architecture Optimization and Control
