FedConPE: Efficient Federated Conversational Bandits with Heterogeneous Clients
Authors: Zhuohua Li, Maoli Liu, John C. S. Lui
IJCAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we conduct extensive experiments to demonstrate the effectiveness of our algorithm. Specifically, we aim to answer the following research questions: 1. For both the single-client and multi-client settings, does our algorithm Fed Con PE outperform existing state-of-the-art algorithms for conversational contextual bandits? 2. How do the number of clients M and the arm set size K affect the performance of Fed Con PE? 3. Does our algorithm use fewer conversations in practice? |
| Researcher Affiliation | Academia | Zhuohua Li , Maoli Liu and John C.S. Lui The Chinese University of Hong Kong {zhli, mlliu, cslui}@cse.cuhk.edu.hk |
| Pseudocode | Yes | Algorithm 1: Fed Con PE Algorithm for client i; Algorithm 2: Fed Con PE Algorithm for server |
| Open Source Code | No | The paper does not provide a direct link or explicit statement about the release of its source code. It only mentions that details are postponed to an extended version. |
| Open Datasets | Yes | The details of data generation and preprocessing are postponed to Appendix G in the extended version of this paper [Li et al., 2024]. Movie Lens-25M [Harper and Konstan, 2015]: A dataset from Movie Lens, a movie recommendation website. Last.fm [Cantador et al., 2011]: A dataset from an online music platform Last.fm. Yelp1: A dataset from Yelp, a website where users post reviews for various businesses. |
| Dataset Splits | No | The paper uses standard datasets and evaluates performance, but does not explicitly state the specific train/validation/test splits (e.g., percentages or counts) used for reproducibility. |
| Hardware Specification | No | The paper does not specify any hardware details (e.g., CPU, GPU, memory, or cloud instance types) used for the experiments. |
| Software Dependencies | No | The paper mentions implementing algorithms and using standard libraries for data processing but does not provide specific software names with version numbers for reproducibility. |
| Experiment Setup | Yes | We randomly select 10 users and calculate their cumulative regret over T = 6,000 rounds. We set the arm set size K = 100 and randomly select K arms from |A| for the client. For the baseline conversational algorithms Con UCB, Arm-Con, and Con Lin UCB, we adopt the conversation frequency function b(t) = 5 log(t) , which adheres to their original papers. We set the number of clients M = 10 and independently select K = 100 random arms for each client. Other parameters remain the same as the single-client setting. ...simulations executed for T = 10,000 under different arm set sizes, ranging from 100 to 300 on the synthetic dataset. |