167 citations · 339 across the 26 of their papers we have counts for
28 papers
Local or Global: Selective Knowledge Assimilation for Federated Learning with Limited Labels
Yae Jee Cho, Gauri Joshi, Dimitrios Dimitriadis
Many existing FL methods assume clients with fully-labeled data, while in realistic settings, clients have limited labels due to the expensive and laborious process of labeling. Li…
The Blessing of Heterogeneity in Federated Q-Learning: Linear Speedup and Beyond
Jiin Woo, Gauri Joshi, Yuejie Chi
When the data used for reinforcement learning (RL) are collected by multiple agents in a distributed manner, federated versions of RL algorithms allow collaborative learning withou…
Federated Minimax Optimization with Client Heterogeneity
Pranay Sharma, Rohan Panda, Gauri Joshi
Minimax optimization has seen a surge in interest with the advent of modern applications such as GANs, and it is inherently more challenging than simple minimization. The difficult…
On the Convergence of Federated Averaging with Cyclic Client Participation
Yae Jee Cho, Pranay Sharma, Gauri Joshi +3
Federated Averaging (FedAvg) and its variants are the most popular optimization algorithms in federated learning (FL). Previous convergence analyses of FedAvg either assume full cl…
FedExP: Speeding Up Federated Averaging via Extrapolation
Divyansh Jhunjhunwala, Shiqiang Wang, Gauri Joshi
Federated Averaging (FedAvg) remains the most popular algorithm for Federated Learning (FL) optimization due to its simple implementation, stateless nature, and privacy guarantees…
FedVARP: Tackling the Variance Due to Partial Client Participation in Federated Learning
Divyansh Jhunjhunwala, Pranay Sharma, Aushim Nagarkatti +1
Data-heterogeneous federated learning (FL) systems suffer from two significant sources of convergence error: 1) client drift error caused by performing multiple local optimization…
Multi-Model Federated Learning with Provable Guarantees
Neelkamal Bhuyan, Sharayu Moharir, Gauri Joshi
Federated Learning (FL) is a variant of distributed learning where edge devices collaborate to learn a model without sharing their data with the central server or each other. We re…
On the Unreasonable Effectiveness of Federated Averaging with Heterogeneous Data
Jianyu Wang, Rudrajit Das, Gauri Joshi +3
Existing theory predicts that data heterogeneity will degrade the performance of the Federated Averaging (FedAvg) algorithm in federated learning. However, in practice, the simple…
Federated Stochastic Approximation under Markov Noise and Heterogeneity: Applications in Reinforcement Learning
Sajad Khodadadian, Pranay Sharma, Gauri Joshi +1
Since reinforcement learning algorithms are notoriously data-intensive, the task of sampling observations from the environment is usually split across multiple agents. However, tra…
Federated Learning under Distributed Concept Drift
Ellango Jothimurugesan, Kevin Hsieh, Jianyu Wang +2
Federated Learning (FL) under distributed concept drift is a largely unexplored area. Although concept drift is itself a well-studied phenomenon, it poses particular challenges for…
Maximizing Global Model Appeal in Federated Learning
Yae Jee Cho, Divyansh Jhunjhunwala, Tian Li +2
Federated learning typically considers collaboratively training a global model using local data at edge clients. Clients may have their own individual requirements, such as having…
Heterogeneous Ensemble Knowledge Transfer for Training Large Models in Federated Learning
Yae Jee Cho, Andre Manoel, Gauri Joshi +2
Federated learning (FL) enables edge-devices to collaboratively learn a model without disclosing their private data to a central aggregating server. Most existing FL algorithms req…
Federated Minimax Optimization: Improved Convergence Analyses and Algorithms
Pranay Sharma, Rohan Panda, Gauri Joshi +1
In this paper, we consider nonconvex minimax optimization, which is gaining prominence in many modern machine learning applications such as GANs. Large-scale edge-based collection…
FedLite: A Scalable Approach for Federated Learning on Resource-constrained Clients
Jianyu Wang, Hang Qi, Ankit Singh Rawat +4
In classical federated learning, the clients contribute to the overall training by communicating local updates for the underlying model on their private data to a coordinating serv…
Leveraging Spatial and Temporal Correlations in Sparsified Mean Estimation
Divyansh Jhunjhunwala, Ankur Mallick, Advait Gadhikar +2
We study the problem of estimating at a central server the mean of a set of vectors distributed across several nodes (one vector per node). When the vectors are high-dimensional, t…
Personalized Federated Learning for Heterogeneous Clients with Clustered Knowledge Transfer
Yae Jee Cho, Jianyu Wang, Tarun Chiruvolu +1
Personalized federated learning (FL) aims to train model(s) that can perform well for individual clients that are highly data and system heterogeneous. Most work in personalized FL…
Best-Arm Identification in Correlated Multi-Armed Bandits
Samarth Gupta, Gauri Joshi, Osman Yağan
In this paper we consider the problem of best-arm identification in multi-armed bandits in the fixed confidence setting, where the goal is to identify, with probability for s…
A Field Guide to Federated Optimization
Jianyu Wang, Zachary Charles, Zheng Xu +50
Federated learning and analytics are a distributed approach for collaboratively learning models (or statistics) from decentralized data, motivated by and designed for privacy prote…
Local Adaptivity in Federated Learning: Convergence and Consistency
Jianyu Wang, Zheng Xu, Zachary Garrett +3
The federated learning (FL) framework trains a machine learning model using decentralized data stored at edge client devices by periodically aggregating locally trained models. Pop…
Adaptive Quantization of Model Updates for Communication-Efficient Federated Learning
Divyansh Jhunjhunwala, Advait Gadhikar, Gauri Joshi +1
Communication of model updates between client nodes and the central aggregating server is a major bottleneck in federated learning, especially in bandwidth-limited settings and hig…
Synergy via Redundancy: Adaptive Replication Strategies and Fundamental Limits
Gauri Joshi, Dhruva Kaushal
The maximum possible throughput (or the rate of job completion) of a multi-server system is typically the sum of the service rates of individual servers. Recent work shows that lau…
Bandit-based Communication-Efficient Client Selection Strategies for Federated Learning
Yae Jee Cho, Samarth Gupta, Gauri Joshi +1
Due to communication constraints and intermittent client availability in federated learning, only a subset of clients can participate in each training round. While most prior works…
Service Rate Region: A New Aspect of Coded Distributed System Design
Mehmet Aktas, Gauri Joshi, Swanand Kadhe +2
Erasure coding has been recognized as a powerful method to mitigate delays due to slow or straggling nodes in distributed systems. This work shows that erasure coding of data objec…
Probabilistic Neighbourhood Component Analysis: Sample Efficient Uncertainty Estimation in Deep Learning
Ankur Mallick, Chaitanya Dwivedi, Bhavya Kailkhura +2
While Deep Neural Networks (DNNs) achieve state-of-the-art accuracy in various applications, they often fall short in accurately estimating their predictive uncertainty and, in tur…