Search Results for author:
Found 17 papers, 2 papers with code
Fair Concurrent Training of Multiple Models in Federated Learning
We show how our fairness-based learning and incentive mechanisms impact training convergence and finally evaluate our algorithm with multiple sets of learning tasks on real world datasets.
Empowering Federated Learning with Implicit Gossiping: Mitigating Connection Unreliability Amidst Unknown and Arbitrary Dynamics
It consists of a parameter server and a possibly large collection of clients (e. g., in cross-device federated learning) that may operate in congested and changing environments.
RSBuilding: Towards General Remote Sensing Image Building Extraction and Change Detection with Foundation Model
RSBuilding is designed to enhance cross-scene generalization and task universality.
Towards Safe Autonomy in Hybrid Traffic: Detecting Unpredictable Abnormal Behaviors of Human Drivers via Information Sharing
Then through extensive experiments on SUMO simulator, we show that our proposed algorithm has great detection performance in both highway and urban traffic.
Network Fault-tolerant and Byzantine-resilient Social Learning via Collaborative Hierarchical Non-Bayesian Learning
On network communication, we consider packet-dropping link failures.
Fast and Robust State Estimation and Tracking via Hierarchical Learning
In both algorithms, we use a novel hierarchical push-sum consensus component.
Towards Bias Correction of FedAvg over Nonuniform and Time-Varying Communications
Specifically, in each round $t$, the link between the PS and client $i$ is active with probability $p_i^t$, which is $\textit{unknown}$ to both the PS and the clients.
Federated Learning in the Presence of Adversarial Client Unavailability
Federated learning is a decentralized machine learning framework that enables collaborative model training without revealing raw data.
Privacy-preserving and Uncertainty-aware Federated Trajectory Prediction for Connected Autonomous Vehicles
Deep learning is the method of choice for trajectory prediction for autonomous vehicles.
Distributed Non-Convex Optimization with One-Bit Compressors on Heterogeneous Data: Efficient and Resilient Algorithms
Federated Learning (FL) is a nascent decentralized learning framework under which a massive collection of heterogeneous clients collaboratively train a model without revealing their local data.
Global Convergence of Federated Learning for Mixed Regression
This paper studies the problem of model training under Federated Learning when clients exhibit cluster structure.
A Non-parametric View of FedAvg and FedProx: Beyond Stationary Points
We discover that when the data heterogeneity is moderate, a client with limited local data can benefit from a common model with a large federation gain.
On Learning Over-parameterized Neural Networks: A Functional Approximation Perspective
When the network is sufficiently over-parameterized, these matrices individually approximate {\em an} integral operator which is determined by the feature vector distribution $\rho$ only.
Securing Distributed Gradient Descent in High Dimensional Statistical Learning
Nevertheless, the empirical risk (sample version) is allowed to be non-convex.
Collaboratively Learning the Best Option, Using Bounded Memory
We say an individual learns the best option if eventually (as $t \to \infty$) it pulls only the arm with the highest average reward.
Distributed Statistical Machine Learning in Adversarial Settings: Byzantine Gradient Descent
The total computational complexity of our algorithm is of $O((Nd/m) \log N)$ at each working machine and $O(md + kd \log^3 N)$ at the central server, and the total communication cost is of $O(m d \log N)$.
Defending Non-Bayesian Learning against Adversarial Attacks
This paper addresses the problem of non-Bayesian learning over multi-agent networks, where agents repeatedly collect partially informative observations about an unknown state of the world, and try to collaboratively learn the true state.
