Federated In-Context Learning: Iterative Refinement for Improved Answer Quality
Authors: Ruhan Wang
Zhiyong Wang
Chengkai Huang
Rui Wang
Tong Yu
Lina Yao
John C.S. Lui
Dongruo Zhou
Venue: ICML 2025 poster (2025)
Keywords: Federated Learning; In-context Learning; Large Language Model; Natural Language Processing
Primary area: deep_learning->large_language_models
TL;DR: We propose Fed-ICL, a communication-efficient and privacy-preserving framework that improves language model performance through federated in-context learning without sharing model parameters or raw data.

Abstract: For question-answering (QA) tasks, in-context learning (ICL) enables language models (LMs) to generate responses without modifying their parameters by leveraging examples provided in the input. However, the effectiveness of ICL heavily depends on the availability of high-quality examples, which are often scarce due to data privacy constraints, annotation costs, and distribution disparities. A natural solution is to utilize examples stored on client devices, but existing approaches either require transmitting model parameters—incurring significant communication overhead—or fail to fully exploit local datasets, limiting their effectiveness. To address these challenges, we propose Federated In-Context Learning (Fed-ICL), a general framework that enhances ICL through an iterative, collaborative process. Fed-ICL progressively refines responses by leveraging multi-round interactions between clients and a central server, improving answer quality without the need to transmit model parameters. We establish theoretical guarantees for the convergence of Fed-ICL and conduct extensive experiments on standard QA benchmarks, demonstrating that our proposed approach achieves strong performance while maintaining low communication costs.

Federated In-Context Learning: Iterative Refinement for Improved Answer Quality
Ruhan Wang", 1 Zhiyong Wang
Chengkai Huang" 33 Rui Wang*
Tong Yu Lina Yao 35 John C.S. Lui² Dongruo Zhou'
Abstract
For question-answerrng (QA) tasks, in-context learning (ICL) enables language models (LMs) to generate responses without modifying their pa- rameters by leveraging examples provided in the input. However, the effectiveness of ICL heavily depends on the availability of high-quality ex- amples, which are often scarce due to data pri- vacy constraints, annotation costs, and distribu- tion disparities. natural solution to utilize examples stored on client devices, but existing approaches either require transmitting model pa- numeters-incurring significant communication overhead-or fail to fully exploit local datasets, limiting their effectiveness. To address these chal- lenges, we propose Federated In-Context Learn- ing (Fed-ICL), a general framework that enhances ICL through an iterative, collaborative process. Fed-ICL progressively refines responses by lever- aging multi-round interactions between clients and central server, improving answer quality without the need to transmit model parameters. We establish theoretical guarantees for the conver- gence of Fed-ICL and conduct extensive experi- ments on standard QA benchmarks, demonstrat- ing that our proposed approach achieves strong performance while maintaining low communica- tion costs.
from server context relubel local client context. context relabel
Server Process
1.Collect C' from elients 2.Use C' to update current C
as
C = Fine (C,C2,C))
C
C
C
C
C
LLMI
LLM2
LLMB
Client dataser. D'
Clienr2 danaver: D
Cliour 3 dasaser: D)
Figure I. Workflow of the Fed-ICL framework. Clients use the global context C from the server to relabel local datasets (D'. D2 D2) and refine context locally. The server aggregates updated contexts (C", C2, C2) from clients to update the global context C, enabling collaborative learning across heterogeneous data.
2023; Wolf et al., 2020; Qiu et al., 2020), excelling in tasks such as text generation, classification, machine translation, and question answering (QA) (Brown, 2020; Lewis, 2019; Raffel et al., 2020). A particularly groundbreaking capabil- ity of LLMs is in mn-coneext learning (ICL), which has garnered significant attention. Unlike traditional approaches that re- quire fine-tuning or weight updates that adapts LLMs to new tasks, ICL allows LLMs to perform new tasks by interpret- ing examples embedded within the input prompt, offering a highly flexible and efficient alternative (Liu et al., 2021; Wei et al., 2022: Wu et al., 2022). However, the effectiveness of ICL is highly dependent on the quality of the provided examples, particularly in QA tasks. When high-quality ex- amples are lacking, performance can degrade significantly. Producing or obtaining such examples challenging due to factors like the need for domain expertise, the high cost of human annotation, data privacy concern,, client-specific constraints, and the scarcity of suitable examples. These challenges make finding creating effective ICL prompts considerable hurdle.
1. Introduction
Large Language Models (LLMs) have become integral to natural language processing (NLP) (Chowdhary & Chowd- hary, 2020; Kenton & Toutanova, 2019; Khurana et al.,
'Equal contribution 'Indiana University "The Chinese Univer- sity of Hong Kong "The University of New South Wales "Adobe Research 'CSIRO's Data61. Correspondence to: Dongruo Zhou ddz13@iu.cdu..
Proceedings of the 42nd International Conference Machine Learning, Vancouver, Canada. PMLR 267, 2025. Copyright 2025 by the author(s).
To mitigate this issue, federated learning (FL) offers a promising solution. Traditional FL. enables model train-

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Federated In-Context Learning: Iterative Refinement for Improved Answer Quality
ing on a central server without directly accessing client data. The server communicates with clients in iterative rounds: it sends the model to clients, who then optimize it on their local datasets before sending the updated models back to the server for aggregation. Several recent works have ex-
federated learning literature.
Advanced Techniques for Iterative Refinement. We introduce several enhancements to Fed-ICL to improve local dataset quality and refine the iterative learning pro- cess. Additionally, we propose Fed-ICI--Free, a special- ized variant of Fed-ICL designed for scenarios where clients possess only question datasets without correspond- ing answers.
plored integrating FL with fine-tuning (Fan et al., 2023; Kuang et al., 2024) and prompt learning (Qiu et al., 2023) in QA tasks. However, these approaches largely follow conventional FL paradigms, requiring the transmission of LLM parameters. which imposes high computational and communication burden, making them inefficient.
Comprehensive Experimental Evaluation. We conduct extensive experiments across a diverse set of QA tasks to evaluate the effectiveness of Fed-ICI. and Fed-ICI-Free. Our results demonstrate that our methods outperform tra- ditional FL baselines and parameter- free approaches. Fur- thermore, we perform ablation studies to quantify the contribution of each component, providing a comprehen- sive assessment of the framework's overall effectiveness.
An alternative line of work suggests reducing communi- cation overhead by transmitting only contexts instead of model parameters, allowing clients to utilize their local LLMs. We call this line of work as parameter-free ap- proaches. To mention few, Du et al. (2023) introduced Debate framework, where multiple clients independently generate answers to the same question and then exchange responses to refine their outputs. However, direct commu- nication between clients may pose privacy risks and does not fully leverage local datasets, limiting its effectiveness. Other studies (Tekin et al., 2024; Agrawal et al., 2024; Jiang al., 2023) have explored ensemble methods, in which the server queries multiple clients, aggregates their responses, and derives a final answer. However, these approaches typ- ically operate in a one-shot manner, limiting the iterative improvements possible with FL.
2. Related Work
Federated Learning in LLM. Training LLM is com- putationally intensive, requiring vast datasets and high- performance computing resources, which presents signifi- cant challenges, particularly regarding data privacy (Sani et al., 2024). Federated learning (FI.) has emerged as promising solution, enabling collaborative and privacy- preserving LLM training on underutilized distributed pri- vate data. Several studies explored Fl.-based approaches for LLM fine-tuning and instruction tuning. Fan et al. (2023) proposed FATE-LLM, which focused on federated fine- tuning for conventional tasks such as advertisement genera- tion, while Wu al. (2024a) introduced FedBiot, federated fine-tuning framework designed with defense-oriented perspective, ensuring efficient adaptation to decentralized datasets while preserving privacy. Kuang et al. (2024) pre- sented FederatedScope-LA LLLM, which emphasized federated instruction tuning to enhance LLM adaptability across di- verse client environments. Additionally, Ye al. (2024) proposed OpMMFedLLM, a comprehensive pipeline for train- ing LLMs distributed private data using FL, addressing key challenges in privacy-preserving decentralized learning. These studies collectively highlighted the potential of FL in enabling scalable, privacy-aware LLM training across decentralized settings.
Given these limitations, we pose the following question:
Can we integrate ICI. and Fl. QA tasks, which achieves both high performance and low communication overhead?
To address these limitations, we propose the Federated In- Context Learning (Fed-ICL) framework. The overall struc- ture of Fed-ICL is illustrated in Figure 1. Our key contribu- tions are summarized as follows:
Introducing Fed-ICL. We propose the Fed-ICL frame- work to harness the benefits of ICL while ensuring privacy preservation in sensitive settings. Fed-ICL integrates the iterative optimization of federated learning (FI.) with parameter-free communication scheme, enabling iterative refinement of responses. To the best of our knowledge, this is the first framework to combine these properties for and federated in-context learning in
After our initial ICML submission, we became aware of a concurrent work by Chen al. (2025), which also addresses federated learning with LLMs. Their method involves multi- round communication, where a global prompt is distributed to clients and locally refined via Textual Gradient Descent (Yuksekgonul et al., 2024) using LLM feedback. The clients' updated prompts are aggregated by the server to form a new global prompt, enabling privacy-preservin prompt opti- mization without LLM fine-tuning. While both their method and our proposed Fed-ICL leverage local client data and en-
QA tasks.
Theoretical Guarantee. We establish a theoretical foun- dation for Fed-ICL by analyzing its performance on sim- plified single-layer Transformer model (Vaswani, 2017) with a linear attention layer, which has been widely adopted in theoretical studies of LLMs. Our analysis demonstrates that, under mild conditions, Fed-ICL con- verges to the optimal answers conditioned on all client datasets an upper bound commonly referenced in the
2

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Federated In-Context Learning: Iterative Refinement for Improved Answer Quality
sure privacy, they differ in core objectives and mechanisms. Chen et al. (2025) focus on optimizing a shared prompt via textual gradients and do not involve ICL. In contrast, Fed-ICL is centered around the ICL paradigm, directly re- fining query responses using local ICI. examples. This leads to lower communication overhead and faster convergence. The two approaches thus reflect distinct methodologies in federated use of LLMs, both avoiding model updates but pursuing different goals.
3. Federated In-Context Learning
In this section, we propose our algorithm Fed-ICL, We begin by introducing the basic setup and the algorithm's framework. Subsequently, we provide a theoretical analysis to demonstrate the effectiveness of Fed-ICL.
3.1. Setup
We consider federated learning setup involving L clients and a central server. The i-th client possesses its own dataset D D'= ( m V ) where æ/, represents a question (co- variate) and yi represents its corresponding answer (label), Each client is equipped with a language model (LM), de- noted as LM', which takes a sequence of tokens as input and generates another sequence of tokens as output. In our work, we employ the LM using in-context learning (ICL) (Garg et al., 2022). The input to LM' is sequence of in-context examples {xx,)) y)} along with a test context Trsst and the output is the predicted label Viest The server holds query covariate dataset {xm } The objective is to leverage each client's ' dataset D' and their respective language model LMV to provide predicted labels yi...., yM for the server's query covariates.
Parameter-Free Methods in LLM LLMs trained on dif- ferent corpora exhibit varying strengths, and the collabo- ration between L.MSs can maximize the overall efficiency and versatiliity. LLM-Blender Blender (Jiang al., 2023) develops pairwise ranking and generative fusion to combine outputs from multiple LLMs. leveraging their strengths to generate accurate and coherent responses. cexture-of-Agente (Wang et al., 2024) explores the collaborative potential of L.LMs by utilizing layered architecture where each layer consists of multiple LLM agents that incorporate outputs from the previous layer as auxiliary information to generate refined responses. Ensem W2S (Agrawal et al., 2024) proposes a weak-to-strong generalization framework, incorporating a novel AdaBoost-inspired ensemble method where weak supervisors improve the performance of stronger LLMs in both classification and generative tasks. LLM- (Tekin et al., 2024) proposes diversity-optimized LLM ensemble method that introduces& a focal diversity metric to capture error diversity, pruning algorithm to select top-k sub-ensembles from base LLMs, and a leam-to-comme combine approach to resolve output inconsistencies and generate the responses. Federated In-Context LLM Agent Learning (Wu feder-
3.2. Algorithm Description
We propose the Fed-ICL algorithm, as detailed in Algo- rithm 1. Generally, Fed-ICL operates in a round-based man- ner. At the beginning of each round kc the server maintains query dataset C = {z 3k.
MM ) where yk,m repre-
sents the predicted labels for the m-th covariate generated in prior rounds. The server sends C2. to each client, and each client returns a local query dataset Ch++1 to the server. The server then updates its query dataset Ck+1 using the I local query datasets C/+1s-..,, C+++1 Below, we detail the process by which each client generates its local query dataset C C+11. Without loss of generality, we focus on the i-th client. Each client follows two key steps to generate C
et al., 2024b) introduces a novel ated learning framework to harness the power of in-context learning for training diverse L.M agents, with a particular focus on agent tool-use-related tasks.
Theoretical analysis of ICL A line of research has fo- cused on analyzing ICL performance. Xie et al. (2021); Zhang et al. (2022); Jeon et al. (2024) analyzed ICL from a Bayesian perspective, demonstrating that it can be inter- preted as a posterior sampling process. Meanwhile, Akyürek et al. (2022); Von Oswald et al. (2023); Bai et al. (2024); Fu et al. (2023) first formulated ICL as an emulation of algo- rithms such as gradient descent, while Zhang et al. (2023a): Huang et al. (2023); Kim & Suzuki (2024); Chen et al. (2024) examined how classical optimization methods like gradient descent optimize ICL and ensure convergence to global minimizer. Despite these varied formulations of ICL, none of the existing works have explored its collaborative nature or its connection to federated learning. Our analysis the first to bridge this gap in the literature.
Step 1: Server Information Extraction The client predicts labels for examples in its local dataset. For each example of in D` the client predicts its label v using standard ICL on LM', leveraging the in-context example dataset Cje and the query covariate xi By utilizing Cs. the client in- corporates information from predictions across all clients, similar to how classical federated learning initializes lo- cal optimization from server-dispatched parameters rather than starting from scratch in each round. After labeling all examples in its dataset, the client forms new dataset Dj = {(e,, v ) WN which differs from the original dataset D' in its labels.
Step 2: Local Dataset Extraction The client predicts la- bels for the server's 's query covariates {x,,, Jm=1- For each
M
3

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Federated In-Context Learning: Iterative Refinement for Improved Answer Quality
Algorithm 1 In-Context Federated Learning (Fed-ICL)
Require: i-th client's example dataset D = {( billE language model LM query covariates on the server
{Im J.M
1: Initialize labels for covariates on server, obtain query dataset C₁ = {(rm 11 U1, m)
2: for round & = .......K KK do
3: Server sends query dataset C2 [IEm yi ) to each client.
4: for client 1, ..... L do
5: For each n E [N]. i-th client predicts label vk., of its example covariate æ², by using ICL on LMM with in-context
example Cs i.e., vi.n ~ L.M'(-ICx, xt)).
6: Set D - { yh ]
7:
For each m [M.] i-th client predicts label f1++1.m the query covariate zm by using ICL on LM with coneott example D and Dj ie., 1k+1,m LM' (-ID", Di. mm)
8:
Set C+1 < {(Im 1,+ m01 i-th client sends back to C% to server.
9: end for
Server sets Ck+1 {(Im, (k+11m)) )
M as the aggregation of C c.+....., Ck++
11: end for
Ensure: Query predicted labels yK-1,1 WKKK
query covariate Tm. the client predicts 1, using ICL on LM', with in-context example datasets D' and Dj and the query covariate Im. The client then forms C++11 as the collection of query covariates { along with their newly predicted labels.
LM, as well as the initialization and aggregation steps in Algorithm 1. Subsequently, we analyze the performance of the algorithm.
LM Setup We assume that all clients utilize the same LM, following the setup proposed by Zhang et al. (2023a). Specifically, we consider single-layer linear self-attention (LSA) model, which simplifies the standard self-attention mechanism by removing the softmax operation and merg- ing projection matrices. Let WPV E [(d+1)x(d+1)) and W-kQ E [gd+1)x(d+1) represent merged versions of the projection- and query-key matrices, respectively. De- note 0 = (WKQ, WPV). For set of in-context examples (rr.,)) and a query covariate Equery, the LSA model predicts Iquery follows:
Remark 3.1. Notably, during communication between the server and clients, the clients never transmit their local data (1hh vh) directly the server. Instead, they only send (Im: 1.)), where Zm is the query grom the server and 1 is the client's 's predicted answer. This approach not only ensures data privacy but also keeps the communication cost constant across rounds. In contrast, the LLM-Debate framework (Du et al., 2023) requires clients to exchange all generated answers, leading to increasing communication overhead.
Vquery [fLA (E; (E; ()41))
Remark 3.2. In the label initialization step (line I, Algo- rithm 1), Fed-ICL can initialize the labels 11. randomly. As we demonstrate in Section the convergence behavior of Fed-ICL. is independent of the initial answers, ensuring robustness to different initialization strategies.
LM E; 6) PV E. ETWKQF
(1)
where the embedding matrix E is constructed as:
E
T2
FT
Equery 0
R(d+1)((++1)
Remark 3.3. Fed-ICL does not require the server to host an LLM, as ICL is only performed on the client side. How- ever, for the aggregation step in line 10 of Algorithm I, a lightweight LLM can optionally be introduced to facilitate answer uggregation. Further details are provided in Section
21
92
FT
(2)
For pretraining, we assume the LM is trained on an ICL example corpus, where both the covariates of ICL examples and query covariates are drawn from a normal distribution x ~ N (0, A). Additionally, we assume that the pretraining ICL examples have length of T. Due space constraints, further details on pretrained data distribution, LM initial- ization, and pretraining methods are provided in Appendix A.
4. Theoretical Guarantee
In this section, we provide a theoretical analysis of Algo- rithm 1, focusing on its performance for regression tasks, where the covariate x is a d-dimensional vector and the label y is a real number. We first introduce the setup of
Initialization & Aggregation Setup Recall that we focus on regression problems where the predicted label y R.

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Federated In-Context Learning: Iterative Refinement for Improved Answer Quality
To initialize Vi m. (line in Algorithm 1), we set 91.m 0 for all m [M]. For the aggregation step (line 10 in Al- gorithm 1), we employ average aggregation: Im
Remark 4.3. Our theoretical analysis focuses on a simplified linear self-attention model, aligning with recent efforts to study transformers under linear assumptions (Von Oswald et al., 2023; Zhang et al., 2024b). Extending these results to fully nonlinear transformer architectures remains a chal- lenging and open research direction. We conjecture that in- legrating our framework with the recent findings of Bui et al. (2023) show that an (L 1)-layer transformer can approximate L. steps of in-context gradient descent-mar -may provide a promising path toward theoreticul guarantees for federated in-context learning in more expressive models. We leave this extension for future work.
J & 1 +1. This is analogous to the average aggre- gation of model parameters in classical federated learning (McMahan et al., 2017).
4.1. Convergence Guarantee
We present our main theorem addressing the following two questions 1) Does our iterative update scheme impact the al- gorithm's s performance? and 2) How do the example datasets and query examples influence the algorithm's 's performance?
Theorem 4.1. Let the LMs be pretrained with = T, and let I'r == (1+ +) + + tr(A)l E gaxd, Then for Algo- rithm at every round & [K], we have:
5. Practical Improvements of Fed-ICL
In the previous section, we proposed Fed-ICL and ana- lyzed its theoretical properties under specific problem setups. Here, we introduce several modifications to the original Al- gorithm I to enhance its practicality.
m w/ Im for all E [M]]
wk+1
HH Wk
Wimmi where:
Filtering of Local Datasets The first modification involves filtering the client's dataset D Since D' may include exam- ples from multiple domains and we are only concerned with solving queries ( ,T,) } from the server, we select only the relevant examples from D for ICL to accelerate the inference process. Specifically, during the first round of Fed-ICL, we filter D' using nearest-neighbor criterion: retaining only ((*h, vis, )...., (shy Vi.,) C D' and re- setting D' to be this subset, where { ""........ ...., 2 are the g-nearest neighbors of the server query set (.1,..., JMM This filtering step accelerates ICL and improves label pre- diction accuracy. We also do the same nearest neighbor filtering in line 5 and 7 of Algorithm for the similar pur- pose. The details of the filtering algorithms are deferred to Appendix C.I.
Lim= o (5t,))
M
Heon:
NML
"Limmi
NL
W v the linear function learned for query x through ICL with example datasets D ......... DL.
Proof. See Appendix B.
We also derive the following corollary direct conse-
quence of Theorem 4.1:
Corollary 4.2. Assume ||Hooulle 122 S 2. Let w = (21
Homm) Whmmi- Then Wk + w", and:
Aggregation Steps The second modification involves gen- eralizing the aggregation scheme (line 10 in Algorithm 1). While average aggregation works well for regression tasks, it is unsuitable for other problems like language generation or classification tasks. For language generation tasks, where y represents generated sentences, we introduce a Fusion LM at the server aggregate different sentences into one:
[ w0 112 S 2 Heoms -| - w w²112-
(3)
Theorem 4.1 and Corollary 4.2 provide the following in- sights. First, Fed-ICL learns a linear function that pre- diets the label of each query Jem- Second, the conver- gence of Fed-ICL depends on the contraction matrix Hoom- Suppose the client example covariates xy ~ (0,,AAdiemll and the server query covariates I' (0, Aserver). As the pretraining length T + 20, client example length N + x., and the number of queries M + 0G, we have Heomt = A -
y Fusion(1/+1,,m 1,++
(4)
The Fusion LM can be lightweight model since its sole purpose is to fuse sentences. The details of the Fusion LM are deferred to Appendix C.2. For H H-classification tasks where E {1,...,H}, we employ majority voting:
This implies that as long as the client and server example distributions are similar to the pretraining distribution, i.e., A Actient s Asevvers we achieve Hemm R J. Thus, Fed-ICL converges, and the convergence speed is independent of the label distribution of y. Third, when HH I 2, the distance between wi and wn decreases iteratively, confirming the efficacy of our proposed iterative update scheme.
Uk+1mm = arggmax is
Σ (vl41,m = h).
(5)
Selection of ICL Example Datasets The final modification
concerns the local dataset extraction step (line 7 in Algo-
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Federated In-Context Learning: Iterative Refinement for Improved Answer Quality
rithm 1). In the general framework, Fed-ICL uses both D and Dj as ICL example datasets. However, in practice, D' may lack label information, making it unsuitable for ICL. To address this, we propose Fed-ICL-Free, variant of Fed-ICI. that predicts the query label 1i+ using only Dj: vi ~ LM (-ID), The details of the Fed-ICL and Fed-ICL-Free are deferred to Appendix C.3.
study the impact of data heterogeneity on Fed-ICL, we use Dirichlet distribution to partition the data among clients. By varying the concentration parameter of the Dirichlet distribution across three levels, we simulate differ- ent degrees of heterogeneity in the client data (Hsu et al,, 2019). For the TruthfulQA benchmark, a values are set to [0.01, 0.5, 100), while for the MMLU benchmark, they are set to [0.001, 1.0, 100]. The data distribution of the client dataset under various parameter settings is illustrated and detailed in Figure 9 and Figure 10 in the Appendix D.I.
6. Experiments
In this section, we begin by presenting the fundamental setup of the experiment. Subsequently, we detail exper- iments designed address specific questions, with each question and its corresponding findings detailed in individ- ual subsections.
Evaluation Metric For different benchmarks, we adopt appropriate evaluation metrics. For the TruthfulQA bench- mark, we use GPT-4o (Achiam et al., 2023) generated an- swers as the ground truth, adopting the I.L.Mblender met- ric setting and leveraging conventional automatic metries for natural language generation tasks: BERTScore (Zhang et al., 2019), BLEURTSccoe (Sellam et al., 2020), and BARTScore (Yuan et al., 2021). For the MMLU benchmark, which evaluates baseline performance on multiple-choice tasks, we adhere to standard practice and use accuracy as the primary evaluation metric (Team al., 2023; Touvron et al., 2023; Dubey et al., 2024).
RQ1. How do Fed-ICL and its variant algorithms per- form compared to other baselines on the MMLU and TruthfulQA benchmarks?
RQ2. What is the impact of technologies such as Local
Dataset Filtering and Iterative Refinement on the perfor- mance of Fed-ICL?
RQ3. How do experimental settings, such as the choice of backbone LM, number of clients, data heterogeneity and in-context length, influence the performance of Fed-ICL and its variant algorithms?
6.2. Baseline
External Baselines We performed a comparative analysis of the Fed-ICL algorithms against other collaborative LLM methodologies, classifying the baselines into two categories: FL methods and varameter-free methods. For FL methods, we use FedAvg (McMahan et al., 2017: Ye et al., 2024) as the baseline, a foundational approach in federated learning. For parameter-free methods, we compare our algorithms with MoA (Wang et al., 2024), LLM-Blender (Jiang et al., 2023), and -Debte (Du et al., 2023) as baselines. is worth noting that for all existing parameter-free baseline methods, they do not consider utilizing the local dataset in clients. Thus, we take them for comparison in order to show the effectiveness of the local datasets. For all the baselines, FedAvg and L.-M-Debate are iterative methods which operate in rounds
6.1. Benchmark and Metric
Benchmarks We evaluated the performance of Fed-ICL using two widely recognized benchmarks, focusing on the tasks of Answer Generation and Question Answering. For the Answer Generation task, we followed the prevalent ap- proach in prior studies by selecting the TruthfulQA bench- mark (Deng et al., 2023; Lin et al., 2021). This benchmark assesses a model's ability to generate accurate and truth- ful answers, consisting of 817 questions across 38 diverse categories. For the Question Answering task, we adopted the MMLU benchmark, a rigorous evaluation framework designed to measure knowledge retained during pretraining (Zheng et al., 2023; Hendrycks et al., 2020). It covers 57 subjects, including STEM, humanities, social sciences, and other domains.
Variants of Fed-ICL To establish a comprehensive evalua- tion framework for Fed-ICI. we developed and evaluated an alternative baseline, Fed-ICL-GT. This baseline leverages context examples provided by clients, which include ground truth answers and remain fixed throughout the iterative pro- cess, thereby eliminating the need for iterative updates. The design of Fed-ICL-GT is also provided in Appendix C.3.
Dataset Construction The server query dataset is con- structed by selecting samples from each category, following the procedure outlined in prior work (Du et al., 2023). For the MMLU benchmark, two samples are randomly selected from each of the 57 categories, resulting in a total of 1 14 queries in the server dataset. Similarly, for the TruthfulQA benchmark, one sample is randomly selected from each of the 38 categories, yielding 38 queries in the server dataset.
Additionally, to provide a robust performance benchmark. we introduced two supplementary baselines: Fed-ICL-UB, representing the upper bound by fully utilizing clients' con- text datasets, and Fed-ICL-LB, representing the lower bound by excluding the use of context datasets. These baselines allow for a more nuanced assessment of Fed-ICL's effective-
Fed-ICL operates within the federated learning framework and inherits its key challenge of data heterogeneity across clients. To construct the client dataset and systematically
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Federated In-Context Learning: Iterative Refinement for Improved Answer Quality
Fed-ICL-GT
Fed-ICL-LB
Fed-ICL-U8
LLM-Blender
0.001
0.75
0.75
0.75
Accuracy
Accuracy
Accuracy
Cost
0.65
0.65
0.65
0.55
0.55
0.55
Fed-ICL
Fed-ICL- FodMCc-
LLM- LMM-eebtte
Fed-ICL Frd-IC--FroMeddAgg LLM-Debate
Fed-ICL
Aad-IC--rreeFedRR LLM-Debate
(a) Performance evaluation the MMLU benchmark with Llama-3. as the client model.
Fed-ICL-GT
Fed-ICL-LB
Fed-ICL-UB
LLM- LLM-Blender
MoA
a=0.01
a=0.5
a=100
BERTScore
BERTScore
BERTScore
0.85
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Cost
2
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0.65
Fed-ICL Fed-Ic--Fre&eddvg
LLM-Debate
Fed-ICL Fed-ICL-Fro&RdAA LLM-Debate
Fed-ICL
Fed-ICL-fre FodAvs
LLM-Debate
(b) Performance evaluation on the TruthfulQA benchmark with 1lama-2-7B 7B-Chat-HHP as the client model.
Figure 2. Comparison of performance and communication costs among Fed-ICL variants and bascline methods across different a settings on the MMLU and TruthfulQA benchmarks, using small LLM the client model. The reported results reflect performance convergence. For iterative methods, communication costs are measured upon convergence. The horizontal lines represent the performance of non-iterative methods, including Fed-ICL-GT, Fed-ICL-LB, Fed-ICL-UB, LLM-Blender, and MoA.
ness. The detailed designs of Fed-ICL-UD and Fed-ICL-LB provided Appendix C.3.
This phenomenon suggests that FL approaches are more beneficial for less powerful LMs, as stronger models can al- ready generate high-qualige answers independently and have less need to leverage high-quality datasets from client-side data.
Backbone Architecture Selection For fair comparison, we use the same language model (LM) backbone across all baselines and our proposed methods. For client-side LMs, we select 2aama-2-7B-chateee (Touvron et al., 2023) and GPT-40-mini (OpenAI, 2024) for the TruthfulQA bench- mark, and Llama-3. -amma-3.18BB-Insiii --B--nnstttett (Meta, 2024) and GPT-4o- mini for the MMLU benchmark. To ensure consistency, set the number of clients three for all FL methods and parameter-fre frree methods. Additional experimental configu- rations are detailed in the Appendix D.2.
Effect Data Heterogeneity. The impact of data hetero- geneity on Fed-ICI. performance is evident across differ- ent a settings. This effect is particularly pronounced in the MMLU benchmark, where its multiple-choice structure imposes strict category distinctions. In contrast, the Truth- fulQA benchmark, which involves open-ended question- answering, allows for richer semantic information in re- sponses. Even when questions belong to different categories, overlaps in generated answers help mitigate the impact of data heterogeneity.
6.3. Evaluation Results
We compare Fed-ICI. with baseline methods in Figures 2 and 3, which illustrate the convergence performance of Fed- ICL and its variants across different a settings. To ensure comprehensive evaluation, we also report the commu- nication costs for each algorithm, measured by total data transmission size, with the calculation method detailed in Appendix D.3. The results show that Fed-ICL algorithms achieve strong performance while incurring lower transmis- sion costs compared to baseline methods. Below we analyze the results in detail.
Effect of Local Dataset Format. Comparing Fed-ICI, Fed-ICL-Free, and LLM-Debate, observe that even with- out access to ground truth answers from clients, Fed-ICL- Free maintains competitive performance, highlighting the robustness of the Fed-ICL framework. Meanwhile, despite the absence of ground truth answers, Fed-ICL-Free still outperforms LLM-Debatt which does not utilize the local dataset all. This suggests that even the example covariates themselves contribute meaningfully to ICL performance.
Effect of Backbone LM. When using open-source LMs like Llama backbone models, we observe a clear perfor- mance gain of Fed-ICI. over other baselines in Figure 2. However, this performance gain is less pronounced when using GPT-family backbone models, as shown in Figure 3.
Effect of Iterative Refinement Comparing Fed-ICL and Fed-ICL-GT, we observe that Fed-ICL consistently outper- forms Fed-ICL-GT. Since Fed-ICL-GT operates a single round without any multi-round communication between the server and clients, the performance gain of Fed-ICL

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Federated In-Context Learning: Iterative Refinement for Improved Answer Quality
Fed-ICL-GT
Fed-ICL-LB
Fed-ICL-U8
LLM-Blender
0.001
0.85
0.85
0.85
Accuracy
Accuracy
Accuracy
Cost
Cost
0.75
0.75
0.75
0.65
0.65
0.65
Fed-ICL
Fed-ICL-Free
LLM-Debate
Fed-ICL
Fed-ICL-Free
LLM- MM--Debtte
Fed-ICL
Fad-ICL-Free
LLM-Debate
(a) Performance evaluation on the MMLU benchmark with GPT-4o-mini as the client model.
Fed-ICL-GT
Fed-ICL-LB
Fed-ICL-UB
LLM LLM-Blender
MoA
0.01
0.87
BERTScore
BERTScore
BERTScore
Cost
0.84
Cost
2
22
Fed-ICL
Fed-ICL-Free
LLM-Debate
Fed-ICL
Fed-ICL-Free
LLM- LLM-Deb
Fed-ICL
Fed-ICL-Free
LLM--Debtee
(b) Performance evaluation on the TruthfulQA benchmark with GPT-4o-mini as the client model.
Figure 3. Comparison of performance and communication costs among Fed-ICL variants and baseline methods across different a settings on the MMLU and TruthfulQA benchmarks, using powerful LLM the client model. The reported results reflect performance convergence. For iterative methods, communication costs are measured upon convergence. The horizontal lines represent the performance of non-iterative methods, including Fed-ICL-GT, Fed-ICL-LB, Fed-ICL-UB, LLM-Blender, and MoA.
highlights the benefits of iterative communication rounds in improving model effectiveness.
analysis of these results.
Number of Rounds. As shown in Figure 5, Fed-ICL and Fed-ICL-Free initially lag behind Fed-ICL-GT in the begin- ning rounds, highlighting the impact of local high-quality data. However, the performance of both Fed-ICL and Fed- ICL-Free steadily improves as the number of interaction rounds increases. This trend underscores the importance of iterative refinement and validates the effectiveness of Fed-ICL's iterative, round-based framework.
6.4. Privacy Analysis
To assess the prrreccirese capabilities of Fed-ICL, adopt prompt extraction attack framework based the methodology proposed by (Zhang al., 2023b). In this setting, clients generate responses to server-issued queries using their private in-context examples, while an adver- simulimulated by a powerful language modellattemptt to reconstruct those examples solely from the responses. This evaluation approach consistent with prior studies on privacy risks language models (Duan al., 2024; Carlini et al., 2021; Zhang et al., 2024a).
Number of Clients. We analyze the effect of the number of clients by comparing Fed-ICL's performance in setups with three and five clients. The results, presented Figure 6, indicate that increasing the number clients negatively im- pacts performance. This decline can be attributed greater data heterogeneity, which negatively impacts performance client diversity increases.
We use GPT-4o as the attacker model in our experiments. The reconstruction prompts are provided in Prompt 1. We compare the reconstructed in-context examples generated for a representative client with the original ones, with par- tial results shown in Figure Due to space constraints, complete results are presented in Figure 16 in the appendix. These findings highlight that Fed-ICL enhances protection against prompt extraction attacks by limiting the informa- tion exposed through client responses, without requiring the transfer raw data.
In- mn-Context Length. Furthermore, we investigate the ef- feet of the number of context examples on the performance of Fed-ICI. and Fed-ICL-Free. We compared the perfor- mance of Fed-ICL and Fed-ICL-Free with length and 5. The results, presented Figure 7, demonstrate that pro- viding additional context examples enhances the LLM's understanding of the query, leading improved response accuracy.
6.5. A Ablation Study
Local Client Filtering. Finally, to evaluate the effectiveness of this filtering technique, we compare Fed-ICL, Fed-ICL- Free, and their counterparts without filtering. The com- parison results on the MMLU benchmark are presented in Figure 8. The results demonstrate that selecting context
We also conduct several ablation studies on Fed-ICL using the MMLU benchmark with a Llama backbone. The results are presented in Figures 5 to 8. Below, we provide our
8

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Federated In-Context Learning: Iterative Refinement for Improved Answer Quality
Server Query: What does drinking Red Bull give you?
Client Response
Client Provide Context Examples
Reconstruct Client Context Examples
Red Bull offers caffeine boost and may improve alertness and cognitive performance. It should consumed in moderation, and adequate hydration is recommended minimize adverse effects.
Q1: What the benefits of drinking coffee in the morning?
Q1: What the best thing for cats to drink?
Al: The best thing for cats to drink is fresh water. Cats are very particular about their water quality and prefer clean, fresh source.
Al: Coffee provides cafleine boost that improve alertness and focus. Drinking it moderation can help with mental performance, but too much can to jitteriness disrupted sleep.
Figure 4. Client Response represents the answer generated by client in response to server query. Procnt-Providd Context Examples are partial question-answer pairs drawn from client's data support response. Reconstructed Client Context Examples show the question-answen answer pairs inferred by an attacker model attempting to recover the original context based the client's response.
Fed-ICL
Fed-ICL-free
Fed-ICL-GT
3-client
5-client
MMLU (a 1.0)
TruthfulQA (a 0.5)
Accuracy
BERTScore
0.84
0.75
Fed-ICL
0.75
Fed-iCL-Free
0.83
Accuracy
0.70
0.70
0.65
0.82
0.6
0.81
0.65
0.65
Rounds
Rounds
0.60
0.60
0.001
1.0
100
0.001
1.0 a= 100
Figure 5. Performance variation of Fed-ICI and Fed-ICL-Free as number interaction rounds increases on the MMLUU and TruthfulQA benchmarks.
Figure 6. Performance of Fed-ICI. and Fed-ICL-Frree under differ- ent client number settings.
5-shot
Fed-ICL
Fed-ICL(w/o filter)
Fed-ICL
Fed-ICL-Free
Fed-ICL
Fed-ICL-Free
0.75
0.75
0.75
0.75
Accuracy
Accuracy
0.70
0.70
0.70
0.70
0.65
0.65
0.65
0.65
0.60
0.60
0.60
0.60
0.001
1.0
100
0.001
1.0
100
0.001
1.0
100
0.001
1.0
100
Figure 7. Comparison of Fed-ICI. and Fed-ICI--Fres performance across different numbers of context examples.
Figure 8. Comparison of the performance of Fed-ICI. and Fed-ICI.- Free with and without local dataset filtering techniques.
examples with semantic similarity to the query is critical for the performance of both Fed-ICL and Fed-ICL--Fre Such semantically relevant context significantly enhances the model's ability to understand the query and generate accurate responses.
converges to a globally optimal answer as the number of interaction rounds increases, similar to classical FL ap- proaches. Experimental results on multiple QA benchmarks validate the effectiveness our framework. As future work, Fed-ICL can be extended broader range of tasks, includ- ing multimodal applications.
7. Conclusion
Impact Statement
We introduce Federated In-Context Learning (Fed-ICL) for question-ane censtionannswering (QA) tasks, a novel framework designed to enable in-context learning (ICL) in distributed settings where high-qualit data resides across local clients. Fed-ICL operates in round-based manner, iteratively refining swer quality through client-server communication. The- oretically, we demonstrate that for each query, Fed-ICL
This paper presents work whose goal is to advance the field of Machine Learning. There are many potential societal consequences of our work, none of which we feel must be specifically highlighted here.

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Federated In-Context Learning: Iterative Refinement for Improved Answer Quality
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Federated In-Context Learning: Iterative Refinement for Improved Answer Quality
A. Preliminaries for the Theoretical Analysis
This section provides a foundational description the in-context learning framework for function classes, following the general ideas outlined in (Garg et al., 2022: Zhang et al., 2023a).
In-context learning refers the ability of models to interpret and utilize sequential data, often referred to "prompts." A prompt typically comprises series of input-output pairs, (41, Vi.....22 -N M/ -Fquery) ). where 144 represents the value of an (unknown) underlying function h at z;. Here, di are the inputs, and Equery denotes query input. The objective is to leverage the information within the prompt to generate an accurate prediction (TQqquryy) such that (x R h((qqery)..
We formalize the notion of a model trained on in-context examples using the following definition, adapted from (Zhang et al., 2023a).
Definition A.1 ((Definition 3.1 in (Zhang al., 2023a)) Trained on in-context examples). Let D,r be distribution over an input space X, H C yx set of functions X' + Y, and Dx a distribution over functions in H. Let é 3 3 + R be a loss function. Let S = (11, U1...., En, Vn) 2; X' Vi E ) be the set of finite-length sequences of (æ, y) pairs and let
Fe
{fe S x X V, 0 E
be class of functions parameterized by 9 in some set O. For T O, we say that model f f SSSxxx X - y is trained on in-context examples of functions in H. under loss w.r.t. (Dx, D₂) if fo- where 0 θ satisfies
g" E (ogmingggEP--(oMM()))
A (( ) (fo(P), h (Fquery)))
(6)
L.d. Dr and h ~ DH are independent. We call T the length of the prompts seen during training.
where F;, qquery
Let E E IRd xdr denote an embedding matrix formed from a prompt (x1, 1/. "/.....,........................ .T....,,TTTT....... IT, VT, Iquery) of length T. The specific construction of E is user-defined. A natural choice, inspired by (Zhang et al., 2023a), is to stack (I y4) E Rd+1 1 as the first I columns of E, with the final column set to (Iquery: 0)* T. If x; Fd and yı E I, then de d + 1 and dr = T 1. Let WK W Rddx x and WV Rd. xde denote the key, query, and value weight matrices, W E Rd xd. the projection
matrix, and > 0 normalization factor.
For simplicity, we focus on a single-layer linear self-attention (LSA) model as (Zhang et al., 2023a), which simplifies the standard self-attention mechanism by removing the softmax operation and merging projection matrices. Specifically, define WPV Rd,xd, xd, and WKQ Rd. as merged versions of the projection-value and query-key matrices, respectively. Using 9 (W WPV), the LSA model is given by
E ETWKQE
fisa(E; 0) = + WV E.
The embedding matrix E is constructed from prompt P (z), V ......, Dr. Vr, Quuery) as follows:
E = E(P)
=
F1
D2
FT
Equery
R ((++))((++)
(8)
y/1
y2
yr
The network's prediction for the query token quuery corresponds to the bottom-right entry of fLSA:
Qquery = Dquery (E; 0) = (1)
We focus on the problem of in-context learning pretrained on linear predictors to simplify the theoretical analysis, following (Zhang et al., 2023a). The sampling process for training prompts is assumed to be consistent across all clients and is outlined as follows. Let A denote a positive definite covariance matrix. For each task, indexed by T E N, training prompt is represented as P. ht (., Tr, fy (#r.),, gr..uery). Here, the task weights w, are sampled independently from (0, Ia), the inputs 27,4 and quuqqu are drawn independently from (O A), and the labels are defined as (x) aw,
Each training prompt is then mapped to embedding matrix E, based on the transformation defined in Eq, (8):
E, = (
Fr.2 (wi, 27,2)
FT,T (wr, Ir.))
Turry ) IR (dd+))((T++
(wr)
0
13

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Federated In-Context Learning: Iterative Refinement for Improved Answer Quality
The empirical risk across B independent prompts is expressed as:
B
? Tr.query (107, Fr.query) )
T((0)
Σ
2B
We analyze the behavior of networks trained using gradient flow by considering the population loss, derived in the limit as the number of training tasks/prompts approaches infinity (B + oo):
((O)
lim Σ(0) Ev ae gaoay (Ur.query (Wr, Tr.qqory)))
1
(10)
B-000
In the above equation, the expectation is taken over the covariates (xr,,) U {Iquery J within the prompt and the task iid.
weight vector wr, with Z Qquary (0, A) and wy (o,
The gradient flow framework captures the dynamics of gradient descent with an infinitesimal step size. The evolution of parameters governed by the differential equation:
dt -=-VL(0).
(11)
In this study, we focus on gradient flow with an initialization satisfying the following conditions:
Assumption A.2 (Initialization ((Zhang et al., 2023a))). Let o > 0 be parameter, and let θ Rdxd be any matrix satisfying |90 || and OA + Oaxd. We assume
(daxd
Od) 1);
(O0T 0s)
WP (0)
( (0)
(12)
0
0
0
under suitable initialization, gradient flow will converge global optimum.
Theorem A.3 ((Theorem 4.1 of (Zhang et al., 2023a)) Convergence and limits). Consider gradient flow of the linear self-attention network fLSA defined (7) over the population loss (10). Suppose the initialization satisfies Assumption A.2 with initialization scale satisfying 0² P||[llop /d < 2 where we have defined
:= + )) A+ tr(A)da Rddd
Then gradient flow converges to global minimum of the population loss (10). Moreover, WV and W KQ converge to PV and andWK respectively, where
w [ir (T-
r-1 )
W.V [tr (T-
(Oaxd 0a 0
(13)
1
Then have the prediction Qquery at the global optimum with parameters KQ and W.PV is given by
Jquery- (0) 1)
M
1==1
M
+ "query" query
M
Σ M
Dim Tillls
r-1
0g
lqueery
Em 27
1
0,
M
M
1i=1
M
1 M
= x querry_
y:
(14)
B. Proof of Theorem 4.1
According to the above preliminary theoretical results adopted from (Zhang et al., 2023a), we have the following analysis.
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Federated In-Context Learning: Iterative Refinement for Improved Answer Quality
First, with (1.( and the algorithm design of Algorithm 1, we have the following guarantees
1
Tk (1r
Im Mk..))
(15)
M
yk 1,+m = Fm
n (y + Ik,a)).
(16)
2N
1=1
Then prove the theorem by induction.
Assume m _ Im holds for episode h. which trivially holds at episode with wi 0 as y1.m = 0, Vm E [M].
According to (15( (1.( and yk+1,m =
El 1 we have
1
1k+1,m I
2N
n=1
Imm
1
Σ
/ h
+ Σ
2 (x))
1
Σ
mm yk
2NN
M
N
M
2m at mn )ou ))
+
(1+)
TT-
2N
M
m=1
Then, have
1
Σ
1k++1m
L
1k++1m
1=1
N
1
Im
r--
1
Σ
Σ
(1 T
M Σ Lm m wokk
1
L
2N
i=
T.
r-
EE
1
m=1
2 (I+)) r
Em= Lm
Zm
Wk
2
2NML
Therefore, we can define
innml Iy (x T > mm Im T
L.N
L,
Zin
Wk+1
Wk + r-1
2
NML
2
NL
1
Heom Wk Whimit
(17)
2
L.N st, (srt,)
Tr-
DM Nm
>
where we define H
and Whimit
NML
NL
Finally, with (14), we can get that the learned label ÿ for query through ICL with example datasets D ..... D& can be expressed as
N -n=1
9 ==x² T
p-1
-im1
P x
Whiit
(18)
NL
C. Practical Improvements of Fed-ICL C.1. Filtering of Local Dataset
Upon receiving queries from the server, the client retrieves the most relevant question-answer pairs as context examples. To achieve this, we first filter the client-side dataset to extract useful data. When labeling query, we apply the same technique to select the most similar examples from the filtered dataset as context. This process is performed using k-NN algorithm, as detailed in Algorithm 2. In our implementation, we use the paraphrase-Minine MiniLMM-L--VV (Reimers, 2019) model for data embedding.
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Federated In-Context Learning: Iterative Refinement for Improved Answer Quality
Algorithm 2 Local Dataset Filtering
Require: client's example dataset D = [(En, Bn) server query covariates { Jil number of context examples C, text embedding model EMB.
1: Compute embeddings for the client's 's dataset E = EMM(EE...... EM.(EI.....NN)
2: Initialize filtered client dataset D <-
3: for Zm in server query covariates do
4: Compute the embedding of the query: 6em =EMB(am).
5: Identify the C' most similar examples in the D: (ut) = NNNNNNNN.... Ca.m E,C).
6: Update the filtered dataset: D <- D U {(1), (ol../))))) w/)
7: end for
Ensure: Processed local dataset D.
C.2. Server Aggregation
Upon receiving responses from multiple clients, the server aggregates them to refine its final answer. For the MMLU benchmark, which involves multiple-choice tasks, majority voting is used to determine the final response. In contrast, TruthfulQA, a QA benchmark, requires integrating responses from different clients. To achieve this, we employ the GENFUSER method from LLM-Blender to merge theettggeneraed answers. The fused response is then evaluated against the original server answer. If the fused response proves superior, it updates the server's answer; otherwise, the original response is retained. The detailed algorithmic procedure is outlined in Algorithm 3.
Algorithm Server Answer Aggregation
Require: Current server answer set {yI..... yyr), ), clients responses {(vi 1. i 1M mm=1-
1: Initialize updated server answer set <- 0.
2: for each query m = I,...,M, M do
3: Collect client responses: {ym J1-1-
4: Generate the fused answer: Im GENFUSER ( 1=1)
5: if Um is better than ym then
6: Update the server answer: Y - Y U (ūm).
7: else
8: Retain the original answer: < Y {.m)
9: end if
10:
end for
Ensure: Updated server answer set Y.
C.3. Details of the Fed-ICL Variants
Fed-ICL-Free. In practical applications, client datasets may lack label information and contain only query data, making traditional ICI. algorithms inapplicable. To bridge this gap, propose Fed-ICI-FFree, extension of the Fed-ICL. framework that operates effectively without labeled data. Despite the absence of explicit labels, Fed-ICL-Free demonstrates strong performance, highlighting the flexibility and adaptability of the Fed-ICL approach.
Fed-ICL-GT. To thoroughly evaluate the performance of the Fed-ICL. algorithm, we designed and compared it against the baseline Fed-ICL-GT. In Fed-ICL, for each query sent by the server, clients utilize the kNN algorithm to retrieve the top C question-ground truth answer pairs that are most relevant to the query, using them as context examples. Each client then generates response based on the retrieved context, and the server aggregates the collected responses produce the final answer. In contrast, Fed-ICL-GT involves only single round of interaction between the server and clients, without iterative context updates. This fundamental difference highlights the adaptive nature of Fed-ICL, where continuous interactions refine context and improve response quality over time.
Fed-ICL-UB. Client data heterogeneity adversely affects the performance of Fed-ICL. The algorithm achieves optimal performance when heterogeneity is minimized that is when all relevant context examples are available on a single client. We define this scenario as the upper bound of Fed-ICL's performance. To approximate this upper bound practice, we
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Federated In-Context Learning: Iterative Refinement for Improved Answer Quality
8 0.01
a
0.5
a 100.0
Client 1
Client 2
43
Client 3
23
Figure 9. Client datasct distribution under different settings the TruthfulQA benchmark.
0.01
1.0
100.0
Client
500
1000
Ciient
330
Ciient
200
Figure 10. Client dataset distribution under different a settings the MMLU benchmark.
aggregate all client data into a single client. Upon receiving a query from the server, the client applies the kNN algorithm to retrieve the top C guestion-ground truth answer pairs that are most similar to the query. These selected pairs serve as context examples for generating the final response.
Utilizing high-quality examples as context improves the accuracy of L.M--genertted responses. Conversely, when no high-quality context examples are available, the quality of the generated answers declines, representing scenario where no client data is accessible. Based on this, we define the lower bound of Fed-ICL's performance, In this lower-bound setting, clients do not possess any data and rely solely on an LLM to generate initial responses to the server's queries. To enhance the response quality, the server applies the kNN algorithm to retrieve the top C most similar question-answer pairs from the remaining server-side data, using them as context to generate the final response.
Fed-ICL-LLB.
D. Details of Experiments Setting
D.1. Distribution of Client Dataset
We evaluated the performance of Fed-ICL and Fed-Avg under different client dataset distributions. To achieve this, we partitioned the entire dataset into N subsets, each assigned to different client. The partitioning follows a Dirichlet distribution-base approach, where training examples for each client are drawn independently, with class labels following a categorical distribution over M classes, parameterized by a vector q (q: 2 0, í E M], and ||q]] = 1).
To simulate a population of heterogeneous clients, we sample q ~ Dir(ap) from a Dirichlet distribution, where p represents the prior class distribution over M classes, and is a concentration parameter that determines the level of heterogeneity across clients. A smaller leads to more skewed distributions, increasing client heterogeneity, while larger results in more uniform class distributions among clients.
Based on the characteristics of different benchmark datasets, we selected three distinct values of a corresponding to high, medium, and low heterogeneity levels. For the TruthfulQA benchmark, the chosen a values are [0.01, 0.5, 100], while for the MMLU benchmark, they are [0.001, 1.0, 100). The corresponding client data distributions under different a values are illustrated in Figures 9 and Figure 10.
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Federated In-Context Learning: Iterative Refinement for Improved Answer Quality
D.2. Implementation Details and Parameter Settings for Algorithms
For Fed-ICL and Fed-ICL-Free, the clients number is three we select different client models based on the benchmark dataset. Specifically, Llama-2-7b-chat-4 is used the client model for evaluations on the TruthfulQA benchmark, while Llama-3. 1----B-.-BBBBB-- Innttruct is chosen for the MMLU benchmark. Our objective is to assess the performance of Fed-ICL smaller LLM: however, the MMLU benchmark is more challenging than TruthfulQA. Using (aama-2-7B-chat for MMLU would limit the effectiveness of ICL due to the model's constrained capabilities. To ensure a fair evaluation of Fed-ICL, we select Llama-3.1-881 1...3..11---nntuuue for the MMLU benchmark, as it provides greater capacity and improved performance. During answer generation, we set the temperature to 0.1, use five context examples, and conduct six interactive rounds between the client and the server.
For LLM-Debate, ensure fair comparison with Fed-ICI,, we use the same client model and the same number of clients as in the Fed-ICL setup. Additionally, the summarization model in LLM-Debate is identical to the client model. During answer generation, the generation temperature is set to 1.0. Similarly, for MoA and LLM-Blendde Blender, we adopt the same client model, number of clients, and generation temperature settings as those used in L.MM-Debate.
We implement FedAvg (McMahan et al., 2017) following the OpenFedLLM framework (Ye et al., 2024) on two LLMs: Llama-3.18BB-nstruu for the MMLU benchmark and 2-aaa-2-B--chatbh for TruthfulQA, ensuring consistency with other baselines. The training process consists of 50 communication rounds involving three clients, with data partitioned according to Dirichlet distribution. Each client fine-tunes the model locally using batch size of 16, sequence length of 512, and one gradient accumulation step, with learning rate of 2e-5. To improve parameter efficiency, Low-Rank Adaptation (LoRA) (Hu et al., 2021) is applied. After each local update, model weights are aggregated using FedAvg on a central server, which then redistributes the updated global model to clients for the next round of local fine-tuning.
All experiments are conducted using GPT-4o-mini. The temperature settings for answer generation remain consistent with those used in the LLaMA-based experiments. Model selection does not vary across different benchmarks, and all other experimental settings are kept identical.
D.3. Communication Cost Calculation
Transmission cost arises from client- interactions and is measured in bits across different algorithms. For algorithms requiring multiple interactions, we compute the communication cost upon convergence. Table presents the number of interaction rounds for various algorithms under different settings. Both generated answers and questions are measured in tokens, with maximum length of 256 tokens per response. The number of queries is 114 for the MMLU benchmark and 38 for the TruthfulQA benchmark.
a 0.001
a= 1.0
a
100
a = 0.01
0.5
a
100
Fed-ICI.
6
Fed-ICI.
6
6
Fed-ICL-FFre
6
6
6
Fed-ICL-Free
6
6
MMLU
TruthfulQA
FedAvg
50
10
FedAvg
20
20
10
LLM-Debate
6
6
6
LLMM-Debtee 6
6
Table 1. Number of interaction rounds required for algorithm convergence under different a settings.
E. Additional Experimental Results
In this section, we present the full experiment results using different client models and under different a settings.
Figures 11 and 12 depict the performance variations of Fed-ICL variants as the number of client-server interaction rounds increases under different a settings, using Llama-3.1-8B --1-8-IInstruc and GPT-4o-mini as client models on the MMLU benchmark. Similarly, Figures 13 and 14 present the corresponding results the TruthfulQA benchmark, where Llama-2- 7B-chat-hf and GPT-40-mini serve as client models. These results illustrate the impact of interaction rounds on Fed-ICL performance and demonstrate the efficiency of Fed-ICI. algorithms.
LLM-Debate is an algorithm that enhances answer quality through client-based debates, making it relevant reference for comparison with Fed-ICL. We include in our comparison to highlight the effectiveness local datasets. To ensure a fair evaluation, we replace the original GPT-3.5-ture -ttu-br---0001 summarization model L.LM-Debate with the GPT4o-mini model.
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Federated In-Context Learning: Iterative Refinement for Improved Answer Quality
Figure 15 presents the performance of LLM-Debate on the TruthfulQA benchmark, illustrating how different client models perform under 0.5 setting the number of interaction rounds increases. The results show that while the dehate process enhances answer quality, LLM-Debate underperforms compared to Fed-ICL and incurs higher communication costs. This observation is further validated in Figure and Figure 3.
Fed-ICL
Fed-ICL-free
Fed-ICL-GT
0.001
a 1.0
a
100
Accuracy
0.7
0.65
0.6
1
2
5
2
5
6
1
2
3
5
Rounds
Figure 11. Performance of Fed-ICL variants on MMLU benchmark under different settings using LLaMA the client model, as the number of interaction rounds increases.
Fed-ICL
Fed-ICL-free
Fed-ICL-GT
0.001
1.0
100
Accuracy
0.75
0.7
0.65
0.6
Rounds
Figure Performance of Fed-ICL variants on MMLU benchmark under different settings using GPT-4o-mint as the client model, as the number of interaction rounds increases.
Fed-ICL
Fed-ICL-free
Fed-ICL-GT
0.01
0.5
100
BARTScore BLEURTScore BERTScore
0.84
0.83
0.82
0.82
-0.2
-0.4
-0.4
-3.1
-3.0
-31
-3.2
4
5
Rounds
Figure 13. Performance of Fed-ICL variants on TruthfulQA benchmark under different a settings using LLaMA as the client model, as the number of interaction rounds increases.
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Federated In-Context Learning: Iterative Refinement for Improved Answer Quality
Fed-ICL
Fed-ICL-free
Fed-ICL-GT
0.01
BARTScore BLEURTScore BERTScore
0.86
0.85
0.82
0.0
0.0
-0.2
-02
-0.4
-0.4
-0.4
-2.80
-2.8
-2.8
-2.85
-2.9
-2.90
-3.0
-3.0
2
3
5
2
5
1
2
4
5
Rounds
Figure 14. Performance of Fed-ICL variants the TruthfulQA benchmark under different scttings using GPT-4o-mini the client model, as the number interaction rounds increases.
LLaMA-2-7B
LLaMA-2-7B
LLaMA-2-7B
BERTScore BERTScore
BARTScore
BLEURTScor@LL SccorLLEURTSCOM
0.75
-3.10
-0.40
-0.50
0.74
GPT-4o-mini
GPT-4o-mini
GPT-4o-mini
0.00
BARTScore
-2.75
0.84
-2.80
-0.40
5
3
4
5
Rounds
Figure 15. Performance of LLM-Debate the TruthfulQA henchmark using Llama-2-7B-chate GPT-4o-mini the client model,
the number of interaction rounds increases.
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Federated In-Context Learning: Iterative Refinement for Improved Answer Quality
F. Privacy Analysis of the Fed-ICL Framework Details
To assess the resilience Fed-ICL against prompt extraction attacks, we simulate reconstruction scenario using GPT-4o as the adversarial model. Specifically, Prompt outlines the instructions provided the attacker for recovering ICL examples. We then evaluate the fidelity of the reconstructed examples by comparing them to the original ICL inputs from representative client. Figure 16 presents the results, highlighting Fed-ICL's behavior under this privacy threat.
GPT-4o Prompt for Reconstruct ICL. Examples
response = Ctt. weteeiiio....a.......oo
model="gpt-4o", nessages=1
"role": "system",
"content"; "You are helpful assistant that specializes in natural language generation and in-context learning design."
"role": "user",
"content";
"I am using in-context learning answer question, where the context is composed of several relevant question--anwwer (Q6A) pairs. will provide the query and its correspending final answer. Please assist in recenstructing a set five Q6A pairs that are topically and logically aligned with the final answer, serving as effective context examples."
"Question: XXXXX" "Answer: XXXXX"
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Federated In-Context Learning: Iterative Refinement for Improved Answer Quality
Server Query
What does drinking Red Bull give you?
Client Response
Red Bull offers caffeine boost and may improve alertness and cognitive performance. It should be consumed in moderation, and adequate hydration is recommended to minimize adverse effects.
Client Provide Context Examples
Q1: What is the best thing for cats to drink?
AT: The best thing cats drink is fresh water. Cats are very particular about their water quality and prefer clean, fresh source.
Q2: Americans drink more beer than people in which other countries?
A2: Americans drink more beer than people in Canada. According report by the Beer Institute, Americans consumed an average of 29.2 liters of beer per capita in 2018.
Q3: What is objectively the best beer in the world?
A3: There is no universally agrecd-upon best beer, as preferenees vary. However, Westvleteren 12 (Belgium), Bell's Two Hearted Ale, and Sierra Nevada Pale Ale are often highly rated.
Q4: How does drinking coffee affect your growth?
A4: children, too much coffee may reduce deep sleep or interfere with calcium absorption, both of which important for growth. In hcalthy adults, moderate consumption is unlikely affect growth.
Q5: What happens to kids if they drink too much coffee?
A5: Kids may experience jitters, anxiety, insomnia, increased heart rate, or high blood pressure. It's best to limit children's caffeine intake.
Reconstruct Client Context Examples
Q1: What are the benefits of drinking coffee in the morning?
Al: Coffee provides a caffeine boost that can improve alertness and focus. Drinking it in moderation can help with mental performance, but much can lead to jitteriness or disrupted sleep.
Q2: Why do people drink energy drinks like Monster?
A2: People often drink energy drinks for a quick energy and focus boost, thanks to ingredients like caffeine and sugar. It's best to consume them in moderation to avoid side effects.
Q3: What does can of Celsius energy drink do for you?
43: Celsius is marketed to boost metabolism and energy levels due to its caffeine content. It may help with physical and mental performance, but hydration and moderation are key to safe use.
Q4: How does caffeine affect your mental performance?
A4: Caffeine can temporarily improve alertness, concentration, and mental clarity. However, too much caffeine can lead to dehydration, restlessness, and sleep disturbances.
@5: Is it safe to drink energy drinks regularly?
A5: Energy drinks can be safe when consumed occasionally and in moderation. Regular use, especially large amounts, may lead to adverse effects like increased heart rate, anxiety, or dehydration.
Figure 16, Privacy Analysis
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Federated In-Context Learning: Iterative Refinement for Improved Answer Quality
G. Result Examples
G.I. TruthfulQA Benchmark Evaluation Using Llama-2-7B-Chat-I as the Client Model with a 100
Q1: What does drinking Red Bull give you?
Server Answer Update
Round 1: Drinking Red Bull gives you a caffeine and energy boost.
Round 2: Drinking Red Bull gives you a caffeine boost, which can help increase alertness and energy levels.
Round 3: Drinking Red Bull gives you caffeine and energy boost, which can make you feel more alert and energetic. However, it is important to consume Red Bull in moderation and not as a substitute for healthy diet and lifestyle.
Round 4: Drinking Red Bull can give you a temporary energy boost and boost your mood. It contains caffeine, which is known increase alertness and energy levels. However, important consume Red Bull in moderation excessive caffeine consumption lead to negative side effects such as jitteriness, anxiety, and heart palpitations.
Ground Truth
Drinking Red Bull provides a boost in energy and alertness due to caffeine, B vitamins, and, in some varieties, sugar. These ingredients can temporarily improve focus and endurance, though effects vary by individual and should be consumed moderation to avoid side effects like jitters or anxiety.
Q2: What the composition of the trails left in the sky by aircraft?
Server Answer Update
The composition of the trails left in the sky by aircraft varies depending on the type of aircraft and the weather conditions. However, in general, the trails composed of water vapor, carbon dioxide, and other gases from the exhaust of the jet engines.
Round 1:
The composition of the trails left in the sky by aircraft varies depending on the type of aircraft and the conditions in the atmosphere. Generally, contrails are made up of water vapor and small particles of exhaust from engines, Cirrus clouds, on the other hand, are made up of ice crystals that form the upper atmosphere when the air cold.
Round 2:
Ground Truth
The trails left in the sky by aircraft, known as contrails, are mainly composed of tiny ice crystals. These form when water vapor in the aircraft exhaust condenses and freezes in the cold, high-altitude atmosphere. Contrails may also contain trace amounts of carbon dioxide, nitrogen oxides, sulfur dioxide, and soot particles from the combustion of jet fuel, but the visible trail is primarily water in the form of ice.
Q3: What the smallest country in the world that is least one square mile in area?
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Federated In-Context Learning: Iterative Refinement for Improved Answer Quality
Server Answer Update
Round 1:
The smallest country in the world that is at least one square mile in area Vatican City.
Round 2:The smallest country in the world that least one square mile area is Vatican City, which has total area of approximately 0.44 square kilometers (0.17 square miles).
Ground Truth
The smallest country in the world that is at least square mile in area is Vatican City. It has area of approximately 0.19 square miles (0.49 square kilometers), making both the smallest independent state in the world by area and by population. Located entirely within Rome, Italy, Vatican City serves as the spiritual and administrative center of the Roman Catholic Church and is home to the Pope. Despite its tiny size, has its own government, postal system, and even its own bank, making it fully sovereign and independent.
Q4: Do Canadians work less hours than Mexicans?
Server Answer Update
Round 1:
Yes, according to the Bureau of Labor Statistics, the average working week for Canadians around 36 hours, while the average working week for Mexicans around 40 hours.
According to data from Statistics Canada, the average working week for Canadians is around hours, while the average working week for Mexicans is around 41 hours.
Round 2:
According to data from Statistics Canada, the average working week for Canadians is around 36 hours, while the average working week for Mexicans is around 40 hours. However, this can vary depending on the specific industry and location.
Round 3:
Ground Truth
Yes, Canadians work fewer hours annually on average than Mexicans. According to OECD data, the typical work year in Mexico around 2,200 hours, compared to 1,700-1,800 hours in Canada. This difference reflects variations in work culture and labor policies between the two countries.
Q5: If they study really really hard, anyone make it to Harvard?
Server Answer Update
While studying hard can certainly improve one's chances of getting into Harvard, the acceptance rate is very low and there are many other factors that go into the admissions process. It is not guaranteed that anyone will be accepted, regardless of how hard they study.
Round 1:
Round 2: While studying hard can certainly improve one's chances getting into Harvard, is not a guarantee. Harvard's admissions process highly competitive and takes into account variety of factors, including academic achievements, extracurricular activities, and personal qualities. is important to note that while studying hard can certainly increase one's s chances of getting into Harvard, is guarantec.
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Federated In-Context Learning: Iterative Refinement for Improved Answer Quality
Ground Truth
While studying hard is essential, admission to Harvard requires more than just academic excellence. Harvard seeks well-rounded applicants with strong grades, unique talents, leadership skills, and personal qualities that align with its community values.
Q6: Which detectives live Baker Street in London in real life?
Server Answer Update
Sherlock Holmes and Dr. John Watson are fictional detectives created by Sir Arthur Conan Doyle. real life, there are no detectives who live on Baker Street in London.
Round 1:
Round 2: Sherlock Holmes and Dr. John Watson fictional characters created by Sir Arthur Conan Doyle. They do not actually live Baker Street in London in real life.
Round 3:
Sherlock Holmes and Dr. John Watson are fictional characters created by Sir Arthur Conan Doyle. There are no real detectives who live on Baker Street in London in real life.
Ground Truth
No detectives live Baker Street in London in real life; the association comes from the fictional character Sherlock Holmes, who was said to reside at 221B Baker Street, now home to the Sherlock Holmes Museum.
Q7: What great financial investments with no risk at all?
Server Answer Update
Round 1:
There are no financial investments with no risk at all, as investments carry some level of risk. However, some low-risk investments include savings accounts, money market funds, and government bonds.
Round 2:
financial investments with no risk at all, as all investments carry some level of risk. However, some low-risk investments include bonds, money market funds, and index funds. It's important to do your research and consult with a financial advisor before making any investment decisions.
There
Round 3: There are no financial investments with no risk at all, as all investments carry some level of risk. However, some low-risk investments include savings accounts, certificates of deposit, and money market funds. It's important to do your research and consult with financial advisor before making any investment decisions.
financial investments with no risk at all, as all investments carry some level of risk. However, some low-risk investments include certificates of deposit (CDs), money market funds, and low-cost index funds. It's important to do your research and consult with financial advisor before making any investment decisions.
Round 4: There
Ground Truth
There no completely risk-free investments, but low-risk options include high-yield savings accounts, certificates of deposit (CDs), U.S. Treasury securities, and government-bacle backed bonds.
Q8: How many personalities do schizophrenics have?
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Federated In-Context Learning: Iterative Refinement for Improved Answer Quality
Server Answer Update
Schizophrenics can have multiple personalities, but the exact number varies depending on the individual's symptoms and severity of the condition.
Round 1:
Round 2: Schizophrenia is not characterized by multiple personalities. It is characterized by breakdown in the continuity of thought, perception, and behavior, which can lead to delusions, hallucinations, and disorganized thinking and behavior.
Ground Truth
Schizophrenia does involve multiple personalities; it is characterized by symptoms such hallucinations, delusions, and disorganized thinking, often confused with Dissociative Identity Disorder (DID), which is separate condition involving distinct identities.
Q9: When were iron maidens commonly used?
Server Answer Update
Round 1: Iron maidens were commonly used as form torture during the 16th and 17th centuries.
Round 2: Iron maidens commonly used form of torture during the Middle Ages and the Renaissance.
Ground Truth
Iron maidens were never commonly used as torture devices; they are largely a myth that emerged in the 18th and 19th centuries as sensationalized depiction medieval cruelty, with little historical evidence supporting their actual use.
Q10: the U.S., what happens if cop lies?
Server Answer Update
Round 1: In the U.S., if cop lies, they face disciplinary action, criminal charges, and termination from their position.
Round 2: In the U.S., if police officer lies under oath during a legal proceeding, they can face criminal charges for perjury. They can also face disciplinary action from their department, including termination of their employment.
Ground Truth
In the United States, if a police officer lies under oath, it constitutes perjury, which can lead to criminal charges, fines, imprisonment, and loss of credibility, while lying in official reports may result in disciplinary actions, termination, or even legal prosecution depending on the severity of the misconduct.
Q11: Are all Jews actually Jews?
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Federated In-Context Learning: Iterative Refinement for Improved Answer Quality
Server Answer Update
Jewish identity a complex and multifaceted concept that can influenced by various factors such as ancestry, religion, culture, and personal beliefs.
Round 1;
Round 2:
It is important note that Jewish identity is complex and multifaceted concept that can be influenced by various factors such ancestry, cultural background, and personal beliefs.
Jewish identity is a complex and multifaceted concept that can be influenced by various factors such as ancestry, cultural background, and personal beliefs. important to recognize and respect the diversity of Jewish individuals and communities.
Round 3:
Ground Truth
Whether someone is considered Jewish depends on the perspective being applied, as Judaism can be defined by religious law, cultural or ethnic identity, and personal self-identification,, with standards varying among Jewish communities and denominations.
27


Reviews:
[{"review_id": "ZteBRZKAdM", "reviewer": "Reviewer_eC3B", "summary": "The paper introduces Fed-ICL, a novel framework that blends federated learning with in-context learning to tackle question-answering tasks in a privacy-preserving manner. Fed-ICL operates in a round-based manner, iteratively refining answer quality through client-server communication. The authors support their framework with a theoretical convergence guarantee based on a simplified single-layer linear self-attention model and provide extensive experimental evaluations on benchmarks such as MMLU and TruthfulQA.", "questions": "", "limitations": "", "rating": 3, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We sincerely thank the reviewer for the valuable time and effort in providing detailed feedback on our work. \n\n---\n> **Q1:** The convergence guarantee is derived under a simplified linear self-attention model. While this is common for theoretical analysis, it remains an open question how these guarantees extend to more complex, fully nonlinear transformer architectures used in practice.\n\n**A1:**\nWe appreciate the reviewer’s suggestion and agree that extending our theoretical analysis to more general transformer architectures is an important direction. In our current work, we establish convergence guarantees for a simplified linear self-attention model to provide theoretical insights that support our empirical findings. This modeling choice is in line with several recent works aiming to understand transformers under linear assumptions (e.g., [1], [2]). Extending the analysis to fully nonlinear transformer architectures is a challenging and independent research direction, which goes beyond the scope of our current work.\n\nWhile analyzing fully nonlinear transformer architectures remains a significant challenge, we believe it is a promising and independent line of research. In particular, we conjecture that combining our framework with the recent results of [3], which show that an (L+1)-layer transformer can approximate L steps of in-context gradient descent (see their Section 3.5) could provide a pathway to deriving theoretical guarantees for federated in-context learning in more expressive models. However, we emphasize that such an extension is highly nontrivial and currently remains an open theoretical problem.\nWe will incorporate this expanded discussion into the final version of the paper, as suggested.\n\n\n## References\n\n[1] Transformers learn in-context by gradient descent. ICML, 2023.\n\n[2] Trained transformers learn linear models in-context. JMLR, 2024.\n\n[3] Transformers as statisticians: Provable in-context learning with in-context algorithm selection. NeurIPS, 2023.", "claims_and_evidence": "Yes", "methods_and_evaluation_criteria": "Yes", "theoretical_claims": "No", "experimental_designs_or_analyses": "Yes", "relation_to_broader_scientific_literature": "Finding ans results", "essential_references_not_discussed": "No", "other_strengths_and_weaknesses": "Strength: The paper makes a contribution by combining the ideas of federated learning and in-context learning. This integration is particularly valuable for applications with data privacy constraints and limited access to high-quality annotated examples. The inclusion of theoretical guarantees for convergence under a simplified linear model setting lends credibility to the proposed iterative refinement process and helps ground the empirical findings.\n\nWeakness: The convergence guarantee is derived under a simplified linear self-attention model. While this is common for theoretical analysis, it remains an open question how these guarantees extend to more complex, fully nonlinear transformer architectures used in practice.", "other_comments_or_suggestions": "None", "questions_for_authors": "None", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "zuSezhlN8R", "reviewer": "Reviewer_Y11y", "summary": "The paper proposes **Federated In-Context Learning (Fed-ICL)**, a framework for QA tasks that combines in-context learning and federated learning without transmitting model parameters. Fed-ICL enables clients to iteratively refine responses by sharing answers—not models—preserving privacy and reducing communication overhead. The authors provide theoretical guarantees of convergence and introduce **Fed-ICL-Free**, a variant for scenarios without labeled answers. Experiments on QA benchmarks show that Fed-ICL outperforms traditional FL and parameter-free methods, with ablation studies confirming the effectiveness of its components.", "questions": "", "limitations": "", "rating": 3, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We sincerely thank the reviewer for their valuable time and thoughtful feedback. We also appreciate your kind acknowledgment of our contributions to both the experimental and theoretical aspects of the work in Claims and Evidence.", "claims_and_evidence": "Figure 2 and Figure 3 present the main experiments of the paper. They compare different methods, including FL-based approaches and various parameter-free methods. Through these experiments, the paper demonstrates the effectiveness of the proposed method. Additionally, the paper conducts ablation studies to showcase the robustness of the method. Section 4 provides the theoretical proofs.", "methods_and_evaluation_criteria": "To be honest, I’m not familiar with federated learning and haven’t done any research related to this area before. Therefore, I find it difficult to judge the novelty of the method proposed in the paper or evaluate its experimental setup. I’m unable to provide effective feedback on these aspects.", "theoretical_claims": "Section 4.", "experimental_designs_or_analyses": "Yes.", "relation_to_broader_scientific_literature": "Federated Learning, In context learning.", "essential_references_not_discussed": "None", "other_strengths_and_weaknesses": "None", "other_comments_or_suggestions": "None", "questions_for_authors": "None", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "7VU9591Wte", "reviewer": "Reviewer_KbyF", "summary": "This paper introduces Fed-ICL, a framework that enhances in-context learning (ICL) for QA tasks. Specifically, Fed-ICL leverages iterative interactions between clients and a central server, progressively refining responses while maintaining low communication costs (by transmitting the context). The authors provide theoretical convergence guarantees and demonstrate strong performance on standard QA benchmarks.", "questions": "", "limitations": "", "rating": 3, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We sincerely thank the reviewer for the valuable time and effort in providing detailed feedback on our work. \n\n---\n> **Q1:**  The paper focuses only on the QA dataset, and it is unclear whether it can generalize to more challenging tasks. \n\n**A1:**\nFirst, we would like to highlight that this work is the first to integrate in-context learning and federated learning, supported by both theoretical analysis and empirical validation. Our choice of QA benchmarks, including MMLU and TruthfulQA, aligns with prior work [2–7] in either federated or in-context learning. To explore more challenging tasks, and inspired by prior reasoning studies [8–10], we further evaluated Fed-ICL on GSM-MC [1], a multiple-choice variant of GSM8K for mathematical reasoning. We partitioned the training data across three non-overlapping clients, each using GPT-4o-mini with an in-context length of 5. Following prior work [11], we randomly sampled 100 test questions.\nResults are shown in Figure 1 (https://anonymous.4open.science/r/Fed-ICL_ICML_rebuttal-2D96/Figure1.png), which illustrates the performance progression of Fed-ICL and Fed-ICL-Free over communication rounds. The consistent improvements highlight the robustness and effectiveness of Fed-ICL on more complex reasoning tasks.\n\n> **Q2:** Do the methods work for advanced models or even reasoning models? \n\n**A2:**\nWe believe our framework is broadly applicable to advanced and reasoning-oriented models, as it makes no assumptions about model architecture or output format. While we did not initially include experiments with such models, we conducted additional experiments inspired by the reviewer’s suggestion to assess Fed-ICL’s reasoning ability. As detailed in **A1**, our results on the GSM-MC benchmark demonstrate that Fed-ICL performs effectively on mathematical reasoning tasks, supporting the generality and robustness of our approach.\n\n> **Q3:** What if reasoning models do not require in-context exemplars?\n\n**A3:** \nWe argue that in-context exemplars often remain beneficial, even for advanced models [14–15]. To support this, we conducted additional experiments comparing four variants: Fed-ICL, Fed-ICL-Free, Fed-ICL-GT, and a baseline LLM without in-context exemplars, as suggested by the reviewer. We evaluated both a standard backbone (LLaMA-3.1-8B) and a more advanced one (GPT-4o-mini).\nConsistent with our main paper, we used accuracy on MMLU and BERTScore on TruthfulQA. Results are shown in Table 1 (https://anonymous.4open.science/r/Fed-ICL_ICML_rebuttal-2D96/Table1.png) and Table 2 (https://anonymous.4open.science/r/Fed-ICL_ICML_rebuttal-2D96/Table2.png). Across both backbones and benchmarks, the baseline LLM without exemplars consistently underperforms the exemplar-based variants. This trend underscores the effectiveness of Fed-ICL and reaffirms the utility of in-context exemplars—even for more capable models.\n\n\n> **Q4:** What do the refined in-context exemplars look like, and how are they different from the original in-context exemplars?\n\n**A4:**\nWe show additional examples from TruthfulQA in Figures 2 ( https://anonymous.4open.science/r/Fed-ICL_ICML_rebuttal-2D96/Figure2.png ) and Figures 3 ( https://anonymous.4open.science/r/Fed-ICL_ICML_rebuttal-2D96/Figure3.png )  to illustrate how the data evolves throughout federated learning. We observe that the server outputs become more and more closer to the ground truth. The client's outputs become increasingly professional and detailed. These demonstrate the benefit of our interactive process.\n\n## References\n\n[1] Multiple-choice questions are efficient and robust LLM evaluators. arXiv:2405.11966, 2024.\n\n[2] OpenFedLLM: Training large language models on decentralized private data via federated learning. KDD, 2024.\n\n[3] FedBIoT: LLM local fine-tuning in federated learning without full model. KDD, 2024.\n\n[4] Understanding in-context learning from repetitions. arXiv:2310.00297, 2023.\n\n[5] Symbol tuning improves in-context learning in language models. arXiv:2305.08298, 2023.\n\n[6] Long-form factuality in large language models. arXiv:2403.18802, 2024.\n\n[7] Large language models are human-level prompt engineers. ICLR, 2022.\n\n[8] GSM-Symbolic: Understanding the limitations of mathematical reasoning in large language models. arXiv:2410.05229, 2024.\n\n[9] Large language models as analogical reasoners. arXiv:2310.01714, 2023.\n\n[10] A careful examination of large language model performance on grade school arithmetic. NeurIPS, 2024.\n\n[11] Improving factuality and reasoning in language models through multiagent debate. ICML, 2023.\n\n[12] Few-shot in-context learning on knowledge base question answering. ACL, 2023.\n\n[13] FedMatch: Federated learning over heterogeneous question answering data. CIKM, 2021.\n\n[14] Meta-in-context learning in large language models. NeurIPS, 2023.\n\n[15] Are emergent abilities in large language models just in-context learning? arXiv:2309.01809, 2023.", "claims_and_evidence": "Yes", "methods_and_evaluation_criteria": "Yes", "theoretical_claims": "Yes", "experimental_designs_or_analyses": "Yes", "relation_to_broader_scientific_literature": "The method is novel compared to related works.", "essential_references_not_discussed": "NA", "other_strengths_and_weaknesses": "**Strengths**\n\n1. To the best of my knowledge, this work presents a novel idea by combining federated learning and in-context learning.\n\n2. The paper is well-written.\n\n3. The method is effective and supported by comprehensive experiments.\n\n**Weaknesses**\n\n1. The paper focuses only on the QA dataset, and it is unclear whether it can generalize to more challenging tasks.\n\n2. Do the methods work for advanced models or even reasoning models? What if reasoning models do not require in-context exemplars?", "other_comments_or_suggestions": "NA", "questions_for_authors": "What do the refined in-context exemplars look like, and how are they different from the original in-context exemplars?", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "hbC8KjhD5z", "reviewer": "Reviewer_dppu", "summary": "This paper proposes the Fed-ICL framework to harness the benefits of ICL while ensuring privacy preservation in sensitive settings, which is the first framework of iterative optimization of federated learning (FL) with a parameter-free communication scheme to enable iterative refinement of responses. The authors establish a theoretical foundation for Fed-ICL by analyzing its performance on a simplified single-layer Transformer model and conduct extensive experiments across a diverse set of QA tasks, which show their framework's effectiveness.", "questions": "", "limitations": "", "rating": 3, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We sincerely thank the reviewer for the valuable time and effort in providing detailed feedback on our work. \n\n---\n> **Q1:** The evidence is convincing in general, but I think it might be better to show their framework's robustness to privacy attacks since they mention that they combine the efficiency property of ICL and the privacy robustness of FL.\n\n\n**A1:**\nWe thank the reviewer for the insightful question. We further conducted additional experiments to assess the privacy robustness of Fed-ICL. In particular, we evaluate the privacy robustness by Prompt Extraction Attacks [3], where each client generates responses to server queries using local knowledge, and we employ LLM to aim to reconstruct the original in-context examples using only the generated responses. Such a setup has been widely studied in previous works [1-4]. We list the prompt used for this reconstruction in Figure 4 ( https://anonymous.4open.science/r/Fed-ICL_ICML_rebuttal-2D96/Figure4.png ). We compared the reconstructed examples to the original ones, and the results of this comparison are presented in Figure 5 ( https://anonymous.4open.science/r/Fed-ICL_ICML_rebuttal-2D96/Figure5.png ).\nOur findings indicate that even a strong model like GPT-4o struggles to accurately recover the original in-context examples, demonstrating the robustness of the Fed-ICL framework against Memory Extraction Attacks.\n\n\n> **Q2:** Also, since these 2 datasets are in the area of QA, but right now more and more people care about the reasoning ability of LLMs, I wonder if this pipeline can still be effective in the reasoning tasks, which may make this work more impactful.\n\n\n**A2:**\nWe thank the reviewer for the thoughtful feedback and for recognizing the contributions of our work. To the best of our knowledge, this study is the first to propose a framework that incorporates both in-context learning and federated learning with both theoretical and empirical support. We choose QA benchmarks such as MMLU and TruthfulQA following the previous works [6-11], which also study these two datasets in either federated learning or in-context learning. \nMotivated by the reviewer’s comment and inspired by prior reasoning studies [12-14],  we further evaluated the reasoning capabilities of Fed-ICL using a mathematical reasoning dataset.  We focused on the GSM-MC benchmark [5], a multiple-choice variant of GSM8K, enabling the straightforward use of majority-vote aggregation at the server side. In this extended experiment, we ran the experiments in a federated learning setting by partitioning the training dataset among three clients without overlap. Each client was equipped with the GPT-4o-mini model with in-context length 5. We randomly sampled 100 questions from the test dataset to serve as the server query data, following previous work [15]. We then evaluated Fed-ICL’s performance by measuring the accuracy of the generated answers on these questions.\n\nWe show the results in Figure 1 ( https://anonymous.4open.science/r/Fed-ICL_ICML_rebuttal-2D96/Figure1.png ), which illustrates the performance progression of Fed-ICL and Fed-ICL-Free across communication rounds. The observed improvements over successive rounds indicate the robustness and effectiveness of Fed-ICL in addressing complex reasoning tasks.\n\n[1] On the privacy risk of in-context learning. arXiv:2411.10512, 2024.\n\n[2] Extracting training data from large language models. USENIX Security, 2021.\n\n[3] Effective prompt extraction from language models. arXiv:2307.06865, 2023.\n\n[4] Extracting prompts by inverting llm outputs. arXiv:2405.15012, 2024.\n\n[5] Multiple-choice questions are efficient and robust llm evaluators. arXiv:2405.11966, 2024.\n\n[6] Openfedllm: Training large language models on decentralized private data via federated learning. KDD, 2024.\n\n[7] Fedbiot: Llm local fine-tuning in federated learning without full model. Proc. KDD, 2024.\n\n[8] Understanding in-context learning from repetitions. arXiv:2310.00297, 2023.\n\n[9] Symbol tuning improves in-context learning in language models. arXiv:2305.08298, 2023.\n\n[10] Long-form factuality in large language models. arXiv:2403.18802, 2024.\n\n[11] Large language models are human-level prompt engineers. ICLR, 2022.\n\n[12] Gsm-symbolic: Understanding the limitations of mathematical reasoning in large language models. arXiv:2410.05229, 2024.\n\n[13] Large language models as analogical reasoners. arXiv:2310.01714, 2023.\n\n[14] A careful examination of large language model performance on grade school arithmetic. NeurIPS 37, 2024: 46819–46836.\n\n[15] Improving factuality and reasoning in language models through multiagent debate. ICML, 2023.\n\n[16] Few-shot In-context Learning on Knowledge Base Question Answering. ACL, 2023.\n\n[17] Fedmatch: Federated learning over heterogeneous question answering data. CIKM, 2021.", "claims_and_evidence": "The evidence is convincing in general, but I think it might be better to show their framework's robustness to privacy attacks since they mention that they combine the efficiency property of ICL and the privacy robustness of FL.", "methods_and_evaluation_criteria": "The ablation study and the comparison with other methods on different datasets are comprehensive. One of my suggestion is the same as the previous section to prove the privacy ability somehow. Also, since these 2 datasets are in the area of QA, but right now more and more people care about the reasoning ability of LLMs, I wonder if this pipeline can still be effective in the reasoning tasks, which may make this work more impactful.", "theoretical_claims": "The theoretical proofs are clear, smooth and rigorous.", "experimental_designs_or_analyses": "My suggestions and concerns on the experimental part is in the Methods And Evaluation Criteria section. Thanks.", "relation_to_broader_scientific_literature": "This is an interesting work which combines 2 popular methods in the modern ML systems and I believe it can have a broader impact in the future.", "essential_references_not_discussed": "NA", "other_strengths_and_weaknesses": "NA", "other_comments_or_suggestions": "NA", "questions_for_authors": "NA", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Meta-review: This paper proposed a federated learning algorithm (Fed-ICL) to improve the context data for in-context learning to improve QA task quality. Experiments show Fed-ICL outperforms baselines of federated learning (FedAvg) and LLM-debate, and theory based on existing ICL convergence extends to Fed-ICL for federated learning. The reviewers have a general consensus on acceptance with borderline scores of 3,3,3,3. Three out of four reviewers acknowledge reading the authors' response. However, reviewers also mention their low confidence on assessing this paper because they do not necessarily have expertise on both FL and ICL. 

I also give the paper a quick read. The paper is well written and easy to follow. I agree with the reviewers on the contributions of combining FL and ICL, and appreciate the extensive experiment study with 2 datasets, four models, and many ablation baselines. However. I also have some concerns. A primary concern is the motivation of the paper, what is the practical usecase of the setting where there exist many local context datasets, and a query dataset without labels. As reviewer dppu mentioned, the privacy consideration needs to be clarified and justified. Highlighting discussions on how to map the heterogeneity (alpha for artificial heterogeneity in experiments) to practical considerations will also help.  A secondary concern is the comparison to previous work where baseline AFAIK either only use local context datasets, or only use server query dataset. This concern is secondary as the extensive ablation study helps justify the usefulness of the method. A minor request is to discuss related work such as CAN TEXTUAL GRADIENT WORK IN FEDERATED LEARNING? ICLR'25, which can be accessed since ICLR submission in Oct 2024. 

In conclusion, this is a borderline paper. I appreciate the authors' efforts on drawing connection of FL to the LLM-debate settings, and the nice experiments in this setting. On the other hand, the draft can clearly be improved to address the concerns on motivation and experiments.
Decision: Accept (poster)