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Found 52 papers, 16 papers with code
Offline Clustering of Linear Bandits: Unlocking the Power of Clusters in Data-Limited Environments
The key challenge in Off-ClusBand arises from data insufficiency for users: unlike the online case, in the offline case, we have a fixed, limited dataset to work from and thus must determine whether we have enough data to confidently cluster users together.
Towards Optimal Heterogeneous Client Sampling in Multi-Model Federated Learning
In this work, we develop a novel convergence analysis of MMFL with arbitrary client sampling methods, theoretically demonstrating the strengths and limitations of previous well-established gradient-based methods.
Interference-Aware Edge Runtime Prediction with Conformal Matrix Completion
Accurately estimating workload runtime is a longstanding goal in computer systems, and plays a key role in efficient resource provisioning, latency minimization, and various other system management tasks.
LLM-Powered Decentralized Generative Agents with Adaptive Hierarchical Knowledge Graph for Cooperative Planning
Instead of fully sharing information from all past experiences, DAMCS introduces a multi-modal memory system organized as a hierarchical knowledge graph and a structured communication protocol to optimize agent cooperation.
Pairwise Elimination with Instance-Dependent Guarantees for Bandits with Cost Subsidy
We introduce the Pairwise-Elimination (PE) algorithm for the known reference arm variant and generalize PE to PE-CS for the subsidized best reward variant.
FedTLU: Federated Learning with Targeted Layer Updates
Federated learning (FL) addresses privacy concerns in training language models by enabling multiple clients to contribute to the training, without sending their data to others.
FedGAT: A Privacy-Preserving Federated Approximation Algorithm for Graph Attention Networks
Cross-client edges arise naturally in such cases and present an interesting challenge to federated training methods, as training a graph model at one client requires feature information of nodes on the other end of cross-client edges.
HumekaFL: Automated Detection of Neonatal Asphyxia Using Federated Learning
BA is one of the primary causes of neonatal death in the world.
FedBaF: Federated Learning Aggregation Biased by a Foundation Model
Foundation models are now a major focus of leading technology organizations due to their ability to generalize across diverse tasks.
FedSPD: A Soft-clustering Approach for Personalized Decentralized Federated Learning
Federated learning has recently gained popularity as a framework for distributed clients to collaboratively train a machine learning model using local data.
Federated Communication-Efficient Multi-Objective Optimization
We study a federated version of multi-objective optimization (MOO), where a single model is trained to optimize multiple objective functions.
RGMDT: Return-Gap-Minimizing Decision Tree Extraction in Non-Euclidean Metric Space
Deep Reinforcement Learning (DRL) algorithms have achieved great success in solving many challenging tasks while their black-box nature hinders interpretability and real-world applicability, making it difficult for human experts to interpret and understand DRL policies.
Neural Combinatorial Clustered Bandits for Recommendation Systems
We consider the contextual combinatorial bandit setting where in each round, the learning agent, e. g., a recommender system, selects a subset of "arms," e. g., products, and observes rewards for both the individual base arms, which are a function of known features (called "context"), and the super arm (the subset of arms), which is a function of the base arm rewards.
FedGraph: A Research Library and Benchmark for Federated Graph Learning
Federated graph learning is an emerging field with significant practical challenges.
Efficient Federated Learning against Heterogeneous and Non-stationary Client Unavailability
Addressing intermittent client availability is critical for the real-world deployment of federated learning algorithms.
Federated Large Language Models: Current Progress and Future Directions
Large language models are rapidly gaining popularity and have been widely adopted in real-world applications.
QMOS: Enhancing LLMs for Telecommunication with Question Masked loss and Option Shuffling
Large Language models (LLMs) have brought about substantial advancements in the field of Question Answering (QA) systems.
Federated Learning with Flexible Architectures
To address this need, this paper introduces Federated Learning with Flexible Architectures (FedFA), an FL training algorithm that allows clients to train models of different widths and depths.
FedAST: Federated Asynchronous Simultaneous Training
Much of the existing work in FL focuses on efficiently learning a model for a single task.
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.
Efficient Contextual LLM Cascades through Budget-Constrained Policy Learning
Recent successes in natural language processing have led to the proliferation of large language models (LLMs) by multiple providers.
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.
CoRAST: Towards Foundation Model-Powered Correlated Data Analysis in Resource-Constrained CPS and IoT
Foundation models (FMs) emerge as a promising solution to harness distributed and diverse environmental data by leveraging prior knowledge to understand the complicated temporal and spatial correlations within heterogeneous datasets.
An LLM-Based Digital Twin for Optimizing Human-in-the Loop Systems
In this paper, we present a case study that employs LLM agents to mimic the behaviors and thermal preferences of various population groups (e. g. young families, the elderly) in a shopping mall.
DYNAMITE: Dynamic Interplay of Mini-Batch Size and Aggregation Frequency for Federated Learning with Static and Streaming Dataset
Federated Learning (FL) is a distributed learning paradigm that can coordinate heterogeneous edge devices to perform model training without sharing private data.
Adversarial Robustness Unhardening via Backdoor Attacks in Federated Learning
Our research, initially spurred by test-time evasion attacks, investigates the intersection of adversarial training and backdoor attacks within federated learning, introducing Adversarial Robustness Unhardening (ARU).
Intelligent Communication Planning for Constrained Environmental IoT Sensing with Reinforcement Learning
Internet of Things (IoT) technologies have enabled numerous data-driven mobile applications and have the potential to significantly improve environmental monitoring and hazard warnings through the deployment of a network of IoT sensors.
Intelligent Communication
Multi-agent Reinforcement Learning
+1
RGMComm: Return Gap Minimization via Discrete Communications in Multi-Agent Reinforcement Learning
This result enables us to recast multi-agent communication into a novel online clustering problem over the local observations at each agent, with messages as cluster labels and the upper bound on the return gap as clustering loss.
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).
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.
GiPH: Generalizable Placement Learning for Adaptive Heterogeneous Computing
Careful placement of a computational application within a target device cluster is critical for achieving low application completion time.
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.
FedRule: Federated Rule Recommendation System with Graph Neural Networks
Much of the value that IoT (Internet-of-Things) devices bring to ``smart'' homes lies in their ability to automatically trigger other devices' actions: for example, a smart camera triggering a smart lock to unlock a door.
FIRE: A Failure-Adaptive Reinforcement Learning Framework for Edge Computing Migrations
In edge computing, users' service profiles are migrated due to user mobility.
Characterizing Internal Evasion Attacks in Federated Learning
However, combining adversarial training with personalized federated learning frameworks increases relative internal attack robustness by 60% compared to federated adversarial training and performs well under limited system resources.
Hierarchical Conversational Preference Elicitation with Bandit Feedback
To achieve this, the recommender system conducts conversations with users, asking their preferences for different items or item categories.
Batch-Size Independent Regret Bounds for Combinatorial Semi-Bandits with Probabilistically Triggered Arms or Independent Arms
Under this new condition, we propose a BCUCB-T algorithm with variance-aware confidence intervals and conduct regret analysis which reduces the $O(K)$ factor to $O(\log K)$ or $O(\log^2 K)$ in the regret bound, significantly improving the regret bounds for the above applications.
Faithful Explanations for Deep Graph Models
Second, our analytical and empirical results demonstrate that feature attribution methods cannot capture the nonlinear effect of edge features, while existing subgraph explanation methods are not faithful.
FedGCN: Convergence-Communication Tradeoffs in Federated Training of Graph Convolutional Networks
Methods for training models on graphs distributed across multiple clients have recently grown in popularity, due to the size of these graphs as well as regulations on keeping data where it is generated.
FedSoft: Soft Clustered Federated Learning with Proximal Local Updating
Traditionally, clustered federated learning groups clients with the same data distribution into a cluster, so that every client is uniquely associated with one data distribution and helps train a model for this distribution.
GCN-SE: Attention as Explainability for Node Classification in Dynamic Graphs
These works, however, do not fully address the challenge of flexibly assigning different importance to snapshots of the graph at different times, which depending on the graph dynamics may have more or less predictive power on the labels.
Combinatorial Multi-armed Bandits for Resource Allocation
In doing so, the decision maker should learn the value of the resources allocated for each user from feedback on each user's received reward.
Interpretable Clustering on Dynamic Graphs with Recurrent Graph Neural Networks
We characterize the optimal decay rate for each cluster and propose a clustering method that achieves almost exact recovery of the true clusters.
Can we Generalize and Distribute Private Representation Learning?
We study the problem of learning representations that are private yet informative, i. e., provide information about intended "ally" targets while hiding sensitive "adversary" attributes.
Reconstructing Actions To Explain Deep Reinforcement Learning
Feature attribution has been a foundational building block for explaining the input feature importance in supervised learning with Deep Neural Network (DNNs), but face new challenges when applied to deep Reinforcement Learning (RL). We propose a new approach to explaining deep RL actions by defining a class of \emph{action reconstruction} functions that mimic the behavior of a network in deep RL.
Online Competitive Influence Maximization
In this paper, we introduce a new Online Competitive Influence Maximization (OCIM) problem, where two competing items (e. g., products, news stories) propagate in the same network and influence probabilities on edges are unknown.
Towards Flexible Device Participation in Federated Learning
Traditional federated learning algorithms impose strict requirements on the participation rates of devices, which limit the potential reach of federated learning.
Network-Aware Optimization of Distributed Learning for Fog Computing
Unlike traditional federated learning frameworks, our method enables devices to offload their data processing tasks to each other, with these decisions determined through a convex data transfer optimization problem that trades off costs associated with devices processing, offloading, and discarding data points.
Distributed, Parallel, and Cluster Computing
Machine Learning on Volatile Instances
Due to the massive size of the neural network models and training datasets used in machine learning today, it is imperative to distribute stochastic gradient descent (SGD) by splitting up tasks such as gradient evaluation across multiple worker nodes.
Observe Before Play: Multi-armed Bandit with Pre-observations
We consider the stochastic multi-armed bandit (MAB) problem in a setting where a player can pay to pre-observe arm rewards before playing an arm in each round.
MOVI: A Model-Free Approach to Dynamic Fleet Management
Since DQNs scale poorly with a large number of possible dispatches, we streamline our DQN training and suppose that each individual vehicle independently learns its own optimal policy, ensuring scalability at the cost of less coordination between vehicles.
On the Real-time Vehicle Placement Problem
Motivated by ride-sharing platforms' efforts to reduce their riders' wait times for a vehicle, this paper introduces a novel problem of placing vehicles to fulfill real-time pickup requests in a spatially and temporally changing environment.
